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PYTHON · APIs · RAG · CUSTOMER DELIVERY

Forward Deployed Engineer Course

At Brolly Academy, we train you to turn a customer’s problem into a working AI application. You will use Python, APIs and RAG, test your application and learn how to deploy it. Our eight-week Forward Deployed Engineer course includes 60 live hours, guided labs, code reviews and one final capstone project with test results and handover documentation.

40 hours of teaching · 20 hours of guided labs and project reviews · Recordings for 6 months

Next batch: Monday, 2 November 2026 · Monday–Friday, 7:30–9:00 PM IST

Trainer: Manish Kumar, AI Application Engineer · 8 years in software development, including 3 years building AI applications

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TrainerManish Kumar

Duration8 weeks · 60 live hours

FeesOnline ₹20,000

ModesClassroom + online

Explore the Course

Curriculum · Projects · Fees · Trainer · Eligibility · FAQs · Learning resources · Learning outcomes · Assignments · Worked capstone · Readiness check

Batch Details

Duration and live hours
8 weeks; 60 hours: 40 hours of teaching and 20 hours of guided labs and project reviews. Class timings
Monday–Friday, 7:30–9:00 PM IST. Reviews and the weekly doubt clinic take place within scheduled live hours. Next batch starts
Monday, 2 November 2026 Trainer
Manish Kumar, AI Application Engineer Online tuition
₹20,000 Classroom tuition
₹25,000 Practice usage budget
Additional ₹1,000–₹2,000 for API/cloud usage. No credits included; spending caps configured before labs. Instalments
50% at enrolment and 50% before Week 5, without an additional instalment charge. Entry skills and equipment
Basic Python, JSON, APIs and debugging; a laptop with 8 GB RAM, 20 GB free storage and installation permissions.

Forward Deployed Engineer Course in Hyderabad

At Brolly Academy, we offer an eight-week, hands-on Forward Deployed Engineer course for learners with basic coding skills. We teach you how to understand a customer’s needs, connect data and APIs, build an AI application and check whether it works. Manish Kumar leads 60 live hours: 40 hours of teaching and 20 hours of guided labs and project reviews.
Join us live online for ₹20,000 or learn in our Hyderabad classroom for ₹25,000. You will complete smaller module assignments and bring your learning together in one final project. Speak with our team to confirm your batch calendar, classroom venue and applicable taxes before enrolling.

Why choose Brolly Academy for FDE training?

Training and project feedback

  • Eight modules from customer discovery to deployment and handover
  • 40 teaching hours and 20 guided lab/review hours
  • One complete capstone with test evidence and documentation
  • One group code-review session each week
  • Two individual capstone reviews per learner

Learning support and flexibility

  • Classroom or live online learning
  • Recordings shared within 24 hours
  • Recording access for six months after completion
  • Learner-group replies within one working day
  • Weekly doubt clinic during scheduled labs
  • Two equal tuition payments with no extra instalment charge

Forward Deployed Engineer Course Curriculum

Our FDE syllabus has eight practical modules. We start with understanding a customer’s problem, then teach you to connect Python, APIs and AI in a small application. You will practise testing, deployment and handover, with one assignment in each module.

Module 1: Customer Discovery and Delivery Scoping

Customer discovery means asking people how they work and where they get stuck. For example, a support team may spend too long finding the right policy. Identify the users, current steps, data owners and cost of mistakes. Then choose one small problem to solve. Write down what success looks like, what the first version will include and what it will leave out. Compare a simple rules-based solution with an AI approach before choosing tools.
  • Discovery interview and stakeholder map
  • Workflow baseline and success metric
  • Build-versus-buy and AI-versus-rules decisions
  • Scope, dependencies, assumptions and delivery risks
 Practical deliverable: Write a one-page brief for a fictional support team. Define an acceptance test, a non-goal and the evidence needed before deployment.

Module 2: Python, SQL, APIs and Data Integration

An API is a way for one application to request information or actions from another. In this module, use Python and SQL to connect an application to data. Read JSON, a common format for exchanging data, and check that requests contain the right fields. Handle login checks, long result lists and slow services. Keep passwords and API keys out of your code. Set clear retry limits so a failed request does not create the same action twice.
  • Python functions, types, exceptions and packages
  • SQL joins and application data models
  • REST APIs, authentication and webhooks
  • Validation, logging, retry limits and idempotency (avoiding duplicate effects when a request is repeated)
 Practical deliverable: Create a ticket lookup API with synthetic records. Test invalid IDs, denied access and a downstream timeout; show a useful error without leaking credentials.

Module 3: LLM Applications and Structured Outputs

A large language model, or LLM, can generate text, but an application still needs rules and checks. Build a support-ticket classifier that returns a category in a fixed format. Save the prompt and model settings so you can compare versions. Check the response before using it, and send uncertain cases to a person. Compare the AI version with a simpler approach on the same examples. Record mistakes, response time and usage cost.
  • Model selection by task, latency and cost
  • Prompt templates and versioned configuration
  • Structured output and schema validation
  • Fallbacks, bounded retries and failure reporting
 Practical deliverable: Build a support-ticket classifier that returns a validated category and explanation. Reject invalid output and send uncertain cases to a review queue.

Module 4: RAG and Permission-Aware Knowledge Systems

RAG stands for retrieval augmented generation. The application first finds relevant information in permitted documents, then gives that information to a model to help it answer. Split documents into smaller pieces, called chunks, and keep their source details. Embeddings are number-based representations used to find related text through vector search. Compare this with keyword search. Check access before returning information, link answers to their sources and make the application say when the evidence is missing or conflicting.
  • Document parsing, chunking and source metadata
  • Embeddings, vector search and retrieval comparison
  • Citations, answerability and document freshness
  • Access filtering and retrieval evaluation
 Practical deliverable: Build a handbook assistant with source links. Evaluate answerable questions, unsupported requests, conflicting policies and documents restricted to another user.

Module 5: Agents, Tools and Human Approval

An AI agent can choose a tool or next step within a workflow. For example, it may look up a support ticket and suggest an update. Tool calling means asking the application to run a defined function. Check the inputs and the user’s permission in code, and require human approval before important changes. Model Context Protocol, or MCP, is introduced as a way to connect tools; it does not replace security checks. Keep the task small enough to inspect and test.
  • Tool calling, state and workflow boundaries
  • Typed inputs and deterministic validation
  • MCP concepts and least-privilege access
  • Approval, duplicate actions and recovery paths
 Practical deliverable: Build a simulated service-desk assistant that proposes a ticket update. Require approval and demonstrate invalid input, denied access and a duplicate request without touching real systems.

Module 6: Enterprise Workflows and Customer-Facing Delivery

Customer integration means connecting your application to the systems a team already uses. Practise with a mock customer relationship management system, or CRM, and a help-desk workflow. Decide which system owns each field and show whether work is waiting, complete or failed. A webhook sends a message when an event happens; a queue holds work until it can be processed. Explain changes, delays and risks in language a customer can understand.
  • CRM and help-desk integration patterns
  • Queues, webhooks and asynchronous status
  • Human review and audit records
  • Stakeholder demos, change requests and adoption
 Practical deliverable: Connect a synthetic intake form to a review queue and mock CRM. Present a change request with its impact on scope, testing and delivery.

Module 7: Evaluation, Security and Operational Reliability

LLM evaluation means testing an AI application on a saved set of examples. Use normal questions, unsupported requests, restricted information and tool failures. Check whether the task succeeds and whether answers are supported by evidence. Test prompt injection: untrusted text that tries to change the application’s instructions. Observability means using logs and traces to understand what happened. Track response time, token usage and errors while keeping private data out of logs. Set clear checks that must pass before release.
  • Task-specific rubrics and labelled test cases
  • Prompt injection and untrusted input boundaries
  • Latency, token usage, traces and regression tests
  • Release gates, fallback behaviour and incident review
 Practical deliverable: Compare two application versions on the same reviewed cases. Record failures, investigate one regression and write a release recommendation supported by evidence.

Module 8: Deployment, Capstone Demo and Handover

Deployment means making your application available in a controlled environment. Package it with Docker and keep settings separate from code. Add a health check to show whether the service is running. Plan how to return to the previous version if a change fails; this is a rollback. Demonstrate a successful request and a failed one. Finish with a handover runbook: clear instructions another developer can use to run, monitor, recover and clean up the application.
  • Docker, environment configuration and cloud deployment
  • Health checks, monitoring and rollback
  • Acceptance demonstration and user handover
  • Portfolio README, architecture and delivery reflection
 Practical deliverable: Deliver the capstone with setup instructions, test evidence, known limits, a cost note and an operator runbook (instructions to run, monitor and recover the application). Explain your contribution and one trade-off you would revisit.

Review the syllabus with our team

Discuss your starting skills, the eight modules and your capstone before enrolling.
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Forward Deployed Engineer Learning Roadmap

Our eight-week learning roadmap takes you from understanding a customer’s problem to presenting a tested application. We guide you through each stage and review your work along the way. You will also need practice time between classes. Our team will help you check your Python readiness and confirm your batch calendar. Brolly Academy FDE roadmap: discover the customer problem, scope, build, evaluate, deploy and hand over the application.
01
Weeks 1-2: Discover, Scope and Connect
  • Interview a fictional stakeholder, map the workflow and write a small delivery brief with a success metric. Identify the data owner, access constraints and work that is out of scope.
  • Build a Python API integration using synthetic records. Demonstrate valid input, invalid input and a service failure before adding a model.
 02
Weeks 3-5: Build, Retrieve and Integrate
  • Add a model-backed task and validate the response. Build retrieval with citations, then test missing evidence and access restrictions.
  • Connect a bounded agent or review workflow to a mock business system. Require approval for writes and practise a stakeholder demonstration.
 03
Weeks 6-8: Evaluate, Deploy and Handover
  • Compare application versions on the same evaluation set. Investigate a regression, inspect traces and define release gates (checks required before deployment) for quality, safety and cost.
  • Package and deploy the capstone in a controlled environment. Present the architecture, normal and failed runs, rollback plan and handover documentation.
We help you learn to explain the customer’s need, demonstrate your application and show the tests behind your decisions. Your capstone gives you practical work to discuss in interviews and a clear view of the skills you should build next.

What will you submit during the eight FDE modules?

In each module, you will submit a practical assignment so we can review how you apply the concepts. The list below shows what you will prepare. Your trainer will explain the instructions and deadlines for your batch.
Module 1 · Delivery brief A fictional stakeholder map, current workflow, success measure, acceptance criteria and one clear non-goal. Module 2 · API boundary A small API over synthetic records, input validation, useful errors and tests for a denied request and timeout. Module 3 · Structured AI output A model-backed classification task with schema checks, uncertain-case review and a simple baseline comparison. Module 4 · Retrieval evidence A permitted document set, source identifiers, cited answers and checks for missing evidence and restricted content. Module 5 · Approved tool action A simulated ticket update with argument validation, authorization, human approval and duplicate-request handling. Module 6 · Integration handoff A synthetic intake-to-review workflow, field ownership notes and a change request explaining scope and test impact. Module 7 · Evaluation report A reviewed set of normal and failed cases, a comparison of two versions, one investigated regression and a release recommendation. Module 8 · Capstone delivery pack Runnable setup instructions, architecture, test evidence, deployment configuration, rollback, cleanup and a handover runbook.

How do 60 live hours fit into eight weeks?

We schedule five 90-minute sessions each week: 7.5 live hours per week and 60 hours across eight weeks. This includes 40 teaching hours and 20 guided lab and project-review hours. Code reviews and doubt clinics are part of these 60 hours. We will confirm holidays and any catch-up sessions in your batch calendar. Plan time outside class to complete assignments, fix errors and document your final project. The time you need will depend on your starting skills and project scope. Independent practice is separate from the 60 live hours.
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What Is a Forward Deployed Engineer?

A forward deployed engineer is a developer who works closely with customers to solve practical problems with software. The work can include understanding a workflow, connecting data and APIs, building an application and helping people use it.For example, a support team may need an assistant that finds the right policy. The FDE talks to the team, checks which documents each user may access, builds the assistant and tests its answers. In an AI-focused role, the model is one part of the solution alongside code, data and permissions.
Forward deployed engineer roles and responsibilities vary by employer. Some focus on early demonstrations; others include deployment, monitoring and customer handover. Read the job description to see how much coding, customer contact, travel and previous experience it requires.The title does not name one tool or vendor certificate. Learning the role means learning how to understand a problem, build carefully and explain your decisions.

How Practical FDE Training Works

01Understand the Requirement Identify who will use the workflow, what happens today and what a useful improvement would look like. Write acceptance criteria before selecting a framework. 02Build a Small Working Version Connect Python, a validated input and an API or dataset. Demonstrate the ordinary software flow before introducing a model or agent. 03Investigate a Failed Case Test a missing field, denied action, unsupported question or service timeout. Record the expected behaviour, observed result and change needed. 04Explain and Improve Present the application and its limits in plain language. Use the same test cases to check the next version and record why the change helped.

Thinking of Forward Deployed Engineer Training in Hyderabad?

You can learn independently or join a guided programme. At Brolly Academy, we give you a weekly learning plan, practical assignments and feedback on your code and capstone.

Learning on your own

  • Choose your own videos, reading and tools
  • Set your own deadlines and practice tasks
  • Find help through documentation and communities
  • Arrange code feedback independently
  • Plan how to test, deploy and document your project

Brolly Academy live FDE training

  • Follow eight modules over eight weeks
  • Join 40 teaching hours and 20 guided lab/review hours
  • Get weekly group code reviews
  • Receive two individual capstone reviews
  • Build a scoped application with tests and handover notes
  • Revise using recordings for six months after completion
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Learning Support and Recording Access

Session recordings
We share recordings within 24 hours. You can access them for six months after completing the course. Feedback on your code
We include one group code-review session each week and two individual capstone reviews for every learner. Help when you get stuck
Ask questions in our dedicated learner group. We respond within one working day and hold a weekly doubt clinic during scheduled lab time.

Where Are Forward Deployed Engineering Skills Used?

We use realistic learning scenarios to show you how these skills apply to different teams. You will practise with synthetic data or sample data you have permission to use.
Team or workflow: Customer support
Problem to solve: Find reliable answers in approved product documentation
Evidence of a useful solution: Source-supported responses, escalation and reviewed failure cases Team or workflow: Internal operations
Problem to solve: Turn repeated intake work into a traceable workflow
Evidence of a useful solution: Validated fields, approval records and recovery from duplicate requests Team or workflow: Sales operations
Problem to solve: Connect a permitted intake process with CRM records
Evidence of a useful solution: Data ownership, safe writes and an audit trail Team or workflow: IT service desk
Problem to solve: Assist ticket investigation without uncontrolled actions
Evidence of a useful solution: Scoped tools, authorization checks and human approval Team or workflow: Document-heavy teams
Problem to solve: Extract information and route exceptions
Evidence of a useful solution: Field validation, a review queue and error examples Team or workflow: Software implementation
Problem to solve: Adapt a platform to customer constraints
Evidence of a useful solution: Integration tests, deployment configuration and a handover runbook

Forward Deployed Engineer Skills You Will Practise

Customer discoveryPRACTICE 01
We teach you to turn a customer conversation into a clear project brief. Python and APIsPRACTICE 02
You will connect applications and data using Python and APIs. Answers backed by sourcesPRACTICE 03
You will learn to find relevant documents and link AI answers to their sources. Controlled tool actionsPRACTICE 04
We teach you to check permissions and require approval before an application changes data. Failure testingPRACTICE 05
You will test missing inputs, unsupported questions and service failures. Application deploymentPRACTICE 06
We guide you through packaging and deploying your application in a controlled environment. Demo and handoverPRACTICE 07
You will explain your work in a demo and write setup, testing and handover instructions.

What should you be able to demonstrate after this FDE course?

Our goal is to help you build one application you can explain from the customer brief to the final handover. We use the following areas to guide your practice and project discussions. Your progress will also depend on your starting skills and independent work.
Scope a useful problem Show who the user is, how the workflow operates, the constraint you chose and the acceptance test that defines success. Integrate an application Explain the API contract, data validation, permitted inputs, authentication boundary and recovery from a failed dependency. Ground an AI response Trace a question to retrieved sources, identify unsupported answers and enforce document access before generation. Control an agent workflow Demonstrate that a tool call is validated and authorized, approval is required for a consequential write and repeated requests do not create duplicate effects. Evaluate a change Compare versions on the same reviewed cases and discuss quality, latency, usage cost and observed failures without inventing performance claims. Deliver and hand over Provide setup, release checks, monitoring ownership, a normal and failed demonstration, rollback steps and a runbook another developer can follow.
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Meet Your Trainer: Manish Kumar

INSTRUCTOR

Manish Kumar

AI Application Engineer · 8 years in software development

Manish Kumar is an AI Application Engineer with eight years of software-development experience, including three years building AI applications, RAG systems and customer integrations. In Manish’s sessions, you will connect Python and API development with customer requirements, testing and deployment decisions. We include weekly group code reviews and two individual capstone reviews so you can discuss your code, understand mistakes and improve your project.
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Forward Deployed Engineer Projects

Five Scenarios, One Capstone You Can Explain

SCENARIO 01

Customer Support Knowledge Assistant

Problem: support staff cannot find consistent answers.

Build, test and deliver

Build retrieval over a small approved handbook with source links and escalation. Test outdated passages, unanswered questions and access restrictions. Deliver a discovery brief, source map, application, reviewed question set and runbook. Compare answer quality with a search-only baseline.
SCENARIO 02

Document Intake and Review Workflow

Problem: a team retypes information from documents.

Build, test and deliver

Extract selected fields from synthetic invoices or intake forms, validate required values and route inconsistent records for review. Test missing fields, duplicate documents and failed downstream writes. Deliver a field schema, review queue, test evidence and recovery instructions.
SCENARIO 03

Service-Desk Agent with Approved Actions

Problem: staff repeat lookup and ticket-update steps.

Build, test and deliver

Build tools against a fictional ticket store, validate caller permissions and require approval before a write. Demonstrate a denied action, invalid argument, timeout and duplicate request. Deliver a tool contract, approval flow, audit record and failure demonstration.
SCENARIO 04

Customer Onboarding Integration

Problem: onboarding information is scattered across forms and systems.

Build, test and deliver

Connect a synthetic intake process to a mock CRM and task queue. Separate data ownership from model suggestions. Test retries, partial failure and inconsistent records. Deliver an integration map, acceptance checklist, stakeholder demo and handover guide.
SCENARIO 05

AI Evaluation and Release Console

Problem: a team cannot explain whether a new prompt or retrieval version is safer to release.

Build, test and deliver

Compare two versions on a fixed, reviewed dataset; display failed cases, latency and usage. Add a release checklist and rollback note. Deliver a reproducible evaluation run and a written recommendation that acknowledges limitations.

Project Review and Assessment

Review area: Problem and scope
Evidence to present: Discovery brief, user workflow and acceptance criteria.
Question your project should answer: Who benefits, and what is deliberately outside the first release? Review area: Implementation
Evidence to present: Runnable repository, architecture and validated API contracts.
Question your project should answer: Can another developer reproduce the application? Review area: Quality and safety
Evidence to present: Reviewed tests, source checks, permissions and failed cases.
Question your project should answer: What does the application do when the input or evidence is insufficient? Review area: Operations
Evidence to present: Deployment notes, usage observations, health checks and rollback.
Question your project should answer: How would someone detect and recover from a problem? Review area: Communication
Evidence to present: Short stakeholder demo, technical README and handover runbook.
Question your project should answer: Can you explain the result, trade-offs and next useful improvement?
What Goes into Your FDE Portfolio?
  • A one-page problem statement and architecture diagram.
  • A repository with setup instructions and safe sample data.
  • Examples of successful, rejected and failed requests.
  • A concise evaluation report and documented limitations.
  • A recorded or live demonstration of your own work, when prepared.
  • A runbook covering operation, recovery and cleanup.
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Worked capstone example: a customer-support knowledge assistant

Example capstone plan: we use this fictional support-team scenario to explain how you can plan, build and assess your final project. You will agree one main capstone with your trainer from the five available scenarios.

1. Customer problem, scope and success criteria

A fictional support team needs consistent answers from an approved handbook. Build a read-only assistant for three topics: delivery, returns and account access. Exclude refunds, account changes and access to real customer records. A useful result includes the supporting document and a clear escalation when the handbook does not answer the question.

2. Synthetic source data and access rules

Create three short documents yourself: a delivery FAQ, a returns policy and a restricted internal escalation note. Keep document_id, version and access_group with every chunk. Use invented names and records; do not upload an employer's handbook or customer data without permission. Your own synthetic source text avoids assuming a third-party dataset licence. Example record: {"document_id":"delivery-01","version":"v1","access_group":"support","text":"Standard delivery takes three working days."}

3. Application architecture

01 · Request A support user submits a question through a small interface or API. 02 · Authorize Application code identifies the user's allowed document groups. 03 · Retrieve Search only permitted chunks and keep the source and version. 04 · Answer Use retrieved evidence; return an unsupported-answer response when evidence is missing. 05 · Validate Check the response shape and source references before displaying it. 06 · Observe Record safe diagnostic information, usage and failures for review.

4. Build sequence and project evidence

  1. Write the discovery brief and acceptance cases before choosing a model.
  2. Load the synthetic documents and build a search-only baseline.
  3. Add access filtering, then test it independently of the model.
  4. Connect the selected model endpoint and validate its structured response.
  5. Keep source identifiers with answers and add an escalation path.
  6. Evaluate normal, unsupported, restricted and failure cases on the same reviewed set.
  7. Package a controlled deployment with health checks, configuration and usage limits.
  8. Demo a successful request and a failed request, then hand over the runbook.

5. Acceptance tests for the capstone

Answerable question Ask about standard delivery. Expected: the answer cites delivery-01 and agrees with its current text. Missing evidence Ask for a policy absent from the handbook. Expected: disclose that the answer is unavailable and escalate. Restricted source Request the internal escalation note as a support user. Expected: restricted content is not retrieved or returned. Untrusted instruction Place an instruction to reveal hidden data inside a source document. Expected: it does not override application access rules or trigger a tool action. Service failure Simulate a model timeout. Expected: a useful failure message, bounded retries and no invented answer. Version change Change the delivery policy and rerun the reviewed cases. Expected: the answer uses the current version and any regression is recorded.

6. Repository, review and operating handover

Organize the portfolio around README, app, sample_data, tests, evaluations, deployment and runbook folders. Include setup commands, environment-variable names without secrets, an architecture diagram, reviewed expected answers, observed test results and known limits. Screenshots must show your actual application; sample expectations must not be presented as measured results. For release review, explain who owns access, source updates, spend alerts and support escalation. Keep the previous working configuration so you can roll back a failed release. After practice, stop unused services, remove temporary resources, revoke disposable credentials and confirm billing resources are closed. Start with the existing ₹1,000–₹2,000 API/cloud practice budget and configured spending caps. This example does not include credits or assume a local laptop can run a large language model. The software stack and model endpoint depend on the batch and permitted service access. For the security checks, consult the OWASP GenAI application risk guidance. For extended project choices, use our FDE project build guides.

Tools and Technologies in the FDE Course

Use Tools to Serve the Workflow

Python Application and integration code SQL Query and validate records REST APIs Connect business systems Git Review and version changes FastAPI Application endpoints Pydantic Validate structured data LLM APIs Task-specific model integration RAG Ground answers in sources Agent Frameworks Bounded tools and state MCP Concepts Tool integration boundaries Docker Package the application Cloud & Observability Deploy, trace and recover

Forward Deployed Engineer Course Fees & Learning Options

Choose how you want to learn with Brolly Academy: live online or in our Hyderabad classroom. Both options cover our eight-week course with 60 live hours. API and cloud practice costs are additional.

Live Online Course

₹20,000
  • Eight weeks of live learning
  • 60 hours: teaching, guided labs and reviews
  • Weekly group code review
  • Two individual capstone reviews
  • Recordings for six months after completion
  • Two payments: ₹10,000 + ₹10,000
CLASSROOM OPTION

Classroom Training

₹25,000
  • Eight-week course in Hyderabad
  • 60 hours: teaching, guided labs and reviews
  • One scoped capstone project
  • Weekly doubt clinic during lab time
  • Two payments: ₹12,500 + ₹12,500
  • Confirm your classroom venue and batch

Practice Budget & Other Options

₹1,000–₹2,000 Budget this amount in addition to your tuition for API and cloud practice.
  • API and cloud credits are not included
  • We configure spending caps before labs
  • Session recordings are included with the live course
  • Contact our team for separate recorded-only or corporate training availability and pricing
Payment details: Pay 50% at enrolment and 50% before Week 5, with no additional instalment charge. Request the applicable GST treatment in your written quotation before payment. Read the refund policy and terms and conditions before payment. An enquiry is not enrolment.
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Forward Deployed Engineer training in Hyderabad and live online

Learn with us in Hyderabad or join live online, depending on your location and study setup. Classroom tuition is ₹25,000 and live online tuition is ₹20,000. Both follow our eight-week syllabus and completion requirements. Our team will confirm availability and your batch calendar.
Classroom in Hyderabad Confirm the FDE classroom and seat availability before travelling. Brolly Academy publishes its training address near JNTU Metro at Metro Pillar A689, 3rd Floor, Dr Atmaram Estates, Hyder Nagar, Hyderabad 500072. Live online Classes are Monday–Friday, 7:30–9:00 PM IST (UTC+05:30). Join with reliable internet, audio access and a laptop with 8 GB RAM, 20 GB free storage and installation permissions. Costs and written confirmation Allow the additional ₹1,000–₹2,000 practice budget. Before payment, confirm applicable GST, any other disclosed charges, instalment dates and the exact course terms in writing.
Check the academy contact details and the current cancellation and refund policy. Ask how the policy applies to your batch and any rescheduling request; an enquiry does not enrol you.

Career Preparation for Forward Deployed Engineer Training

Present Your Work with Evidence

We teach you to explain your project, the choices you made and the problems you solved. You will practise discussing requirements, debugging and presenting a delivery plan. Use the areas below to prepare your portfolio and interview answers. Ask our team which additional career-preparation sessions are available for your batch. We do not guarantee a job, interview or salary.
Resume Building: Describe your contribution, scope and verified results without invented customer impact. Role Research: Separate junior engineering opportunities from experienced FDE ownership roles. Technical Interviews: Explain APIs, retrieval, tool boundaries, failures and deployment choices. Project Practice: Build and debug one scoped application end to end. Customer Demonstrations: Show a normal task, a failed case and the next useful improvement. System Design: Discuss data flow, access, latency, cost and recovery trade-offs. Application Planning: Compare your evidence with each vacancy rather than applying by title alone. Mock Interview Preparation: Rehearse concise answers and technical follow-up questions. Communication Skills: Translate technical risks into decisions a stakeholder can understand. Delivery Problem Solving: Prioritize blockers and propose a testable next step.

Student Testimonials and Academy Reviews

Selected Reviews from Other Academy Courses

These reviews describe learners’ experiences on the academy courses named below. They are not testimonials for the new FDE course or for Manish. Read the linked source and course context when comparing your learning options.
Rakesh THRGenerative AI | Google Review
The trainers explain topics clearly and patiently, with hands-on exercises that make complex concepts easier to understand.

View Google Source Pranav PranavGenerative AI | Google Review
Sandeep sir made Generative AI concepts very easy. Real-time practice helped me a lot.

View Google Source Sana HariGenerative AI | Google Review
I Recently Completed the Generative AI Training at Brolly Academy and it was an excellent experience.

View Google Source PAVANGenerative AI | Google Review
Sandeep sir explains step by step in simple English. After modules, we worked on real-time projects.

View Google Source Poojasri BaruOnline Classes | Google Review
I liked the online classes. Sandeep sir gives examples for every topic.

View Google Source Roshan MahapatraAzure Data Factory | Google Review
Brolly Academy provides the best Azure Data Factory training program in Hyderabad.

View Google Source

Learner Community

Our dedicated learner group gives you a place to ask questions and discuss your work. Share a small example, explain what you expected and tell us what you tried. We respond within one working day. Please use sample data and keep passwords, API keys and private customer or employer information out of the group.

Prerequisites & Eligibility

Forward Deployed Engineer Course Eligibility

Practice Environment and Lab Setup

Area: Local development
What to prepare: Python environment, editor, Git and the project’s dependency list.
What to verify: A second setup can run the documented example without hidden files. Area: Data and integrations
What to prepare: Synthetic records, JSON examples and a mock API or approved data source.
What to verify: No private employer or customer data is needed for the learning task. Area: Model services
What to prepare: An authorized model endpoint or the alternative specified for the lab.
What to verify: Access, usage limits, billing owner and service availability are known. Area: Application testing
What to prepare: Normal, invalid and denied-input examples with expected results.
What to verify: Changes can be compared against the same cases. Area: Deployment
What to prepare: Project configuration, environment variables and a controlled deployment target.
What to verify: Secrets stay out of source code; rollback and resource cleanup are documented.
Before your first paid lab, we will confirm the accounts you need and help you understand usage limits. API and cloud credits are not included in tuition. Allow an additional ₹1,000–₹2,000 for practice usage.

What You Need Before Joining

Python: functions, collections, exceptions, packages and a virtual environment. Data handling: read JSON, validate required fields and understand basic SQL queries. APIs: understand requests, responses, authentication and common HTTP errors. Debugging: reproduce a failure, inspect an error and test a small change. Git basics: track your work and explain changes without publishing secrets. Study setup: a laptop that runs the development environment and reliable internet. Authorized access: confirm API/cloud accounts, service availability and usage ownership before labs. Practice time: allow independent time for building, testing and writing your project documentation.

Who Should Join Forward Deployed Engineer Training?

Software developers: extend API and debugging experience into customer-facing AI delivery. Solutions and implementation engineers: strengthen hands-on coding, evaluation and deployment evidence. Cloud and data professionals: connect operational knowledge with useful model-backed workflows. QA and automation engineers: learn the application flow behind AI behaviour and build stronger evaluation skills. Technical founders and consultants: scope a focused solution and explain quality, cost and implementation risk. Students with coding foundations: build a credible project and investigate suitable junior roles before targeting experienced FDE positions.

Are you ready for the FDE labs? Try this self-check

Before joining, try five small tasks with synthetic data. This is a self-check for preparation, not an admission examination or a graded course assignment. Practice input: {"ticket_id":"T-101","priority":"high","status":"open"}
  1. Write a Python function that accepts the record and reports whether all three fields are present.
  2. Reject a record with a missing ticket_id and explain the error clearly.
  3. Explain how a GET request differs from a request that changes a ticket, and why authentication is required.
  4. Write a simple SQL query to find open, high-priority tickets in a fictional table.
  5. Save your work in Git and write the commands needed to run it in a clean environment.
 If these tasks are new to you, speak with our team about building your Python, JSON, SQL and API foundations first. If you can complete them with documentation, bring your work to the demo and tell us where you got stuck. We will help you decide whether this course fits your current skills.

Course scope and further-study topics

The eight-week focus Customer discovery, Python/API integration, model-backed applications, retrieval, controlled tool workflows, evaluation, deployment and handover. Further specializationOur eight-week course focuses on building and delivering applications. Training large models from scratch, managing large GPU clusters, full Kubernetes administration and advanced fine-tuning require separate study. Responsible use Use authorized accounts and permitted data. Security exercises teach scoped application checks; completing them does not certify legal compliance or make a project production-safe in every environment.

Should you choose self-study, this course or a longer bootcamp?

Self-study Suitable when you can plan your own practice, debug independently and arrange feedback. Compare the time you can commit and how you will review your project. Our live FDE courseChoose this option if you have basic coding skills and want an eight-week plan, 60 live hours, guided labs, weekly group code reviews and two individual capstone reviews. A longer foundations program Consider this when you need substantial programming preparation or a broader software/cloud curriculum before taking ownership of an AI application. Compare actual hours, review access, assessment and total costs.
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Forward Deployed Engineer Career Opportunities

Choose by Responsibilities, Not Only Job Titles

Forward Deployed Engineer: build and adapt software around customer needs, then support deployment and adoption. AI Solutions Engineer: connect business requirements with a technical solution; inspect whether the role is pre-sales, delivery or both. Applied AI Engineer: integrate models, retrieval and evaluation into an application people use. Implementation Engineer: connect a platform to customer systems and document a reliable rollout. AI Integration Developer: build APIs, data contracts and controlled workflow connections. Customer Engineer: troubleshoot technical adoption and translate field issues into actionable engineering feedback. RAG Application Developer: prepare sources, improve retrieval and test grounding and access boundaries. AI Evaluation Engineer: create repeatable cases, review failures and investigate quality regressions. Cloud Application Engineer: package, deploy and operate applications with existing cloud engineering skills. Technical Consultant: communicate options and implementation trade-offs with evidence. Junior software or AI application roles: a possible starting path for coding-ready freshers; eligibility depends on each vacancy. Senior FDE or architect: a longer-term direction requiring substantial delivery experience beyond one course.

Forward Deployed Engineer Salary in India: Evidence by Experience

Employer-advertised examples checked on 8 October 2026. These are individual job postings, not a salary survey, placement record or guaranteed offer. Experience and example: Fresher / no professional experience
Advertised annual salary: No verified zero-experience FDE range in the examples reviewed
Requirements to compare: Build coding and portfolio evidence; compare junior software and applied-AI openings
Source: Do not treat experienced-role figures as a fresher offer
Experience and example: 1 year listed | Chat360, Gurgaon
Advertised annual salary: INR 8-10 lakh
Requirements to compare: Coding, communication and customer-facing AI solution building
Source: View employer job listing Experience and example: 2 years listed | TechUp Labs, India remote
Advertised annual salary: INR 10-25 lakh
Requirements to compare: Python, APIs, debugging and practical workflow integration
Source: View employer job listing Experience and example: 6+ years | Alpha Nodus, Bengaluru
Advertised annual salary: INR 20-40 lakh
Requirements to compare: Senior engineering plus customer-facing delivery experience
Source: View employer job listing
Location, prior delivery experience, technical depth and customer responsibilities affect compensation. Check whether an offer is fixed salary or total compensation and confirm the current vacancy details. A degree or certificate alone does not establish eligibility; each employer sets its own requirements.

Qualifications and Skills for Forward Deployed Engineer Jobs

Course admission and hiring eligibility are different. A learner can develop these skills before meeting the experience requirement of a particular FDE vacancy.
Software foundations: be ready to demonstrate Python, API integration, debugging and maintainable code. Customer understanding: explain a business workflow, clarify requirements and communicate technical trade-offs. Applied AI: show how models, structured data and retrieval fit into an application you can test. Delivery evidence: present your repository, integration tests, deployment notes and project decisions. Experience and education: check each employer’s stated requirements rather than treating one course certificate as universal eligibility.
For example, TechUp Labs’ employer-posted FDE vacancy lists two years of experience and emphasizes Python, APIs, debugging, AI integration and communication. This is a job-research reference, not a placement partnership. Requirements and vacancy availability can change.

Companies with Forward Deployed Engineering Roles

Explore the responsibilities behind the title. These links are research references, not claims of academy hiring partnerships, guaranteed interviews or ongoing vacancies.
OpenAI: customer-facing production deployments – the referenced role asks for 5+ years of relevant experience. Palantir: engineering career opportunities – compare platform, customer and location requirements. Gushwork: FDE role reference – inspect hands-on workflow delivery expectations. Level AI: deployment role reference – compare integration, guardrail and experience requirements. Chat360: India FDE example – read the customer-facing coding requirements. TechUp Labs: India remote FDE example – compare Python, integration and stakeholder skills.

Forward Deployed Engineering: Market Signals and Learning Priorities

What the Role Descriptions Suggest

Delivery goes beyond a prototype: OpenAI's role description includes customer discovery, technical delivery and production outcomes. Practise the full sequence on a small project. Engineering fundamentals remain important: TechUp Labs' posting emphasizes Python, APIs, debugging and business workflows. Model familiarity does not replace those skills. Customer communication is part of the job: Chat360's posting combines solution building with stakeholder work. Practise asking useful questions and explaining trade-offs. Experience levels vary substantially: compare the actual requirements of each opening before choosing a course or salary target. Title growth is not a placement guarantee: these examples show the scope of selected roles, not measured market growth or a promise of hiring. Build evidence that remains useful across related engineering titles.

FDE vs AI Engineer vs Solutions Engineer: Which Path Fits?

Titles overlap. Compare responsibilities and experience requirements in the actual vacancy.
Path: Forward Deployed Engineer
Typical emphasis: Customer-specific implementation and end-to-end delivery
Useful evidence: Discovery brief, working integration, deployment and handover Path: Applied AI Engineer
Typical emphasis: Model-backed product or application features
Useful evidence: Application code, evaluation results and operational behaviour Path: Solutions Engineer
Typical emphasis: Technical fit, demonstrations and customer discussions; scope varies
Useful evidence: Requirements analysis, solution design and a clear demo Path: ML Engineer
Typical emphasis: Model and data pipelines or ML production systems
Useful evidence: Data handling, modelling/evaluation and production pipeline skills Path: Cloud / DevOps Engineer
Typical emphasis: Infrastructure, release automation and operations
Useful evidence: Deployment, monitoring, access and recovery evidence
A Generative AI course offers broader model concepts; this FDE path emphasizes customer delivery. Choose RAG Training for retrieval specialization, or the Azure AI Course for Azure-focused application development.

Course Completion and Certification

To earn your Brolly Academy course completion certificate, attend at least 80% of live sessions, submit all eight module assignments and score at least 70% on the capstone rubric. If we approve a missed session, you must catch up with the recording and complete its assignment. We allow two capstone resubmissions within 30 days of feedback, with no additional assessment fee. Our certificate confirms that you completed Brolly Academy’s course requirements; it is not a vendor certification or a job guarantee.
Brolly Academy sample certificate for completing the Forward Deployed Engineer Course.

Evidence to Prepare for Course Completion

  • Coursework: complete the agreed assignments and attendance requirements.
  • Personal contribution: explain the code and design decisions you made.
  • Capstone: demonstrate a normal run and a meaningful failed case.
  • Evaluation: provide reviewed tests and explain limitations honestly.
  • Handover: include setup, operations, rollback and cleanup instructions.
  • Optional external credentials: choose by your target employer and verify the current provider requirements and fees.
 We encourage you to present your certificate alongside your project, code, test results and demo. This gives an employer a clearer view of what you can do. The certificate shown here is an academy illustration, not a vendor credential.

Forward Deployed Engineer Course FAQs

1. What is a Forward Deployed Engineer course?

Our Forward Deployed Engineer course trains you to turn a customer’s problem into a small, tested application. At Brolly Academy, we teach customer discovery, Python, APIs, AI applications, RAG, testing, deployment and handover. You will learn over eight weeks with 60 live hours, guided labs and one capstone project.

2. What does FDE stand for?

Here FDE means Forward Deployed Engineer. Some organizations use related titles or different expansions. Read the role description: customer delivery, coding responsibilities, travel and required experience matter more than the abbreviation alone.

3. Is this course only about generative AI?

No. AI integration is part of the curriculum, but the wider focus is delivering a useful application. You also practise requirements, APIs, data handling, tests, deployment, stakeholder communication and operational handover.

4. Who is this FDE course for?

Our course is designed for developers, implementation and solutions engineers, cloud and data professionals, technical consultants and students with basic coding skills. Talk to our team during a free demo so we can help you check whether you need Python or API preparation first.

5. Can freshers join?

Yes, freshers with basic coding skills can join our course and build a portfolio project. We help you practise the technical and communication skills used in customer delivery. Many FDE vacancies require work experience, so also consider suitable junior software and AI application roles as your first step.

6. Do I need a computer-science degree?

We focus on your practical readiness in Python, JSON, APIs and debugging rather than requiring one particular degree for this course. Speak with our team about your background before enrolling. Employers set their own degree and experience requirements.

7. How much Python should I know?

You should be able to write Python functions, use lists and dictionaries, handle errors and install packages. Practise reading JSON, the format many APIs use to exchange data. You do not need to know every library, but you should be able to investigate a small coding error. Use the readiness check above to spot any basics you need to practise first.

8. Do I need SQL, frontend or cloud experience?

Basic SQL and API knowledge help. Frontend and cloud familiarity are useful, but the course is not a complete replacement for full-stack or cloud engineering training. We use a scoped application so you can connect the parts and identify what to study next.

9. What is the course duration?

The Forward Deployed Engineer course duration is eight weeks, with 60 live hours: 40 hours of teaching and 20 hours of guided labs and project reviews. Classes run Monday–Friday, 7:30–9:00 PM IST. Plan extra time outside class for practice and assignments.

10. What is the Forward Deployed Engineer course fee?

The confirmed tuition is INR 20,000 for live online learning and INR 25,000 for classroom learning. Request written details covering taxes, lab or API usage, support and completion requirements before payment.

11. Can I attend online or in Hyderabad?

You can choose live online FDE training or classroom learning in Hyderabad. Both tuition options are listed on this page. Before enrolling, confirm the classroom venue or online joining details, batch calendar and laptop requirements with the academy.

12. Is there a free demo?

Yes. Select Book a Free Demo to open our enquiry form, or contact us on WhatsApp. Our team will help you review your starting skills, learning goals, syllabus, trainer and batch arrangements.

13. Who is the trainer?

Manish Kumar is an AI Application Engineer with 8 years in software development, including 3 years building AI applications, RAG systems and customer integrations. The course includes a weekly group code review and two individual capstone reviews per learner.

14. Are EMI, recorded videos or corporate training available?

We share session recordings within 24 hours, and you can access them for six months after completion. You can pay tuition in two equal instalments: 50% at enrolment and 50% before Week 5, with no extra instalment charge. Contact our team for separate corporate training arrangements.

15. Are model APIs and cloud credits included?

API and cloud credits are not included. Budget an additional ₹1,000–₹2,000 for practice usage. Spending caps are configured before labs; use small experiments and clean up resources after practice.

16. Will I build five projects?

We offer five project scenarios so you can choose a suitable capstone with your trainer. You will build one main capstone, supported by smaller module assignments. A capstone is your final project, where you bring together the skills you have learned.

17. What should the capstone contain?

Your FDE portfolio should show the problem, your code and the evidence that it works within its stated limits. Include a clear README, an architecture diagram, safe sample data, setup steps and test results. Add deployment settings, a cost note, rollback steps and a handover guide. Explain your own contribution and label simulated customer work honestly.

18. Does the course cover RAG?

Yes. You learn retrieval augmented generation, or RAG: finding relevant information in documents before asking a model to answer. The syllabus includes document preparation, embeddings, vector search, source links and access checks. You also test missing information and unsupported answers. A source link helps a reader check an answer, but it does not prove the answer is correct.

19. Does it include AI agents and MCP?

The curriculum includes bounded tool workflows, validation, state and human approval, with MCP introduced as an integration concept. These interfaces do not replace authentication or business rules. The practical focus is a controlled workflow you can inspect and test.

20. Which frameworks will I use?

We focus on Python, SQL, APIs, JSON, Git, Docker and a cloud deployment workflow. Git tracks changes to your code, while Docker packages your application so it can run consistently. We select retrieval and AI tools to suit the learning task. Ask our team for the exact tools used in your batch.

21. Is this an official Palantir or OpenAI course?

No. This is Brolly Academy training. Employer role descriptions are linked as research references, not affiliations, endorsements, recruitment partnerships or official courseware. The course does not award a vendor credential.

22. Will I receive a certificate?

Complete all 8 module assignments, maintain at least 80% live attendance and score at least 70% on the capstone rubric. Approved missed sessions require recording catch-up and the associated assignment. Two capstone resubmissions are allowed within 30 days of feedback without an additional assessment fee.

23. Is placement guaranteed?

No. The course includes portfolio and interview-preparation topics, but completion does not guarantee a job, interview, placement or salary. Hiring depends on your skills, prior experience, vacancies and the employer’s selection process.

24. What salary can a fresher expect?

The salary section links individual employer advertisements and separates their experience requirements. No verified zero-experience FDE salary band is claimed. Do not apply a one-year or senior-role salary to a fresher; compare actual entry-level offers and their compensation structure.

25. How is FDE different from a solutions engineer?

The titles overlap. Many FDE roles emphasize hands-on implementation and production delivery, while some solutions-engineering roles emphasize technical fit and pre-sales demonstrations. Read the actual responsibilities to understand where ownership starts and ends.

26. How is this different from the Generative AI course?

The FDE course organizes learning around customer delivery from discovery to handover. A broader Generative AI course may be a better first step for model concepts and general foundations. Choose by your current skills and next work goal, not by collecting similar course titles.

27. Can I use my employer’s data in a project?

Only with explicit authorization and appropriate safeguards. Synthetic or permitted sample data is the default. Do not upload private documents, credentials or customer records to model services or learner groups without permission.

28. How are projects evaluated?

Use task-specific acceptance criteria and a reviewed set of normal and failed cases. Assess functionality, grounding, access controls, reliability and clarity of handover. A single automated score is not enough to establish that an application is safe or useful in every situation.

29. Will the course guarantee expertise in two months?

We give you an eight-week learning plan, guided practice and feedback on one final project. Becoming confident takes continued practice and experience. Your trainer’s feedback will help you identify the next skills to work on.

30. How do I enrol or request the syllabus?

Request our syllabus through the form or book a free demo. We will help you review the trainer, calendar, teaching hours, costs, support and completion requirements before payment. Please read our refund policy and terms. Sending an enquiry does not enrol you.

Can I replay sessions and get code feedback?

Recordings are available within 24 hours and remain accessible for six months after completion. The course includes one group code review each week and two individual capstone reviews per learner, within the 60 live hours.

What laptop do I need?

Bring a laptop with 8 GB RAM, 20 GB free storage and permission to install development tools. Online learners also need reliable internet.

How to Join the Forward Deployed Engineer Course

01
Discuss Your Starting Point Tell the team about your Python and API experience, the role you are targeting and your preferred learning format. 02
Book a Free Demo Review a sample learning task, the eight-module plan and the project options. Ask which foundations you should complete first. 03
Review Your Batch Details Get the assigned trainer, start date, teaching hours, support arrangements and total costs in writing. Confirm recordings, missed-session options and project-review access. 04
Confirm Enrolment Read the terms and conditions and refund policy, then follow the academy’s official payment instructions. An enquiry alone does not enrol you.
Ask About the Next FDE Batch or call +91 81868 44555.

FDE Learning and Career Resources

Understand the Role and Learning Path Prepare for FDE Careers Practise Customer Delivery

More questions about choosing an FDE course

Does this course include a paid internship or a client placement?

Our course includes teaching, guided labs, project reviews and one capstone. We do not promise a paid internship or client placement as part of this offer. Our team will explain the scope and written terms of any separate opportunity.

Do I need an expensive GPU laptop?

You need a laptop with 8 GB RAM, 20 GB free storage and permission to install development tools. We use an approved model service or the alternative specified for each lab. You are not expected to train or run a large model on your laptop.

Can I compare a shorter course with a longer FDE bootcamp?

Yes. When comparing an FDE course with a longer bootcamp, check the starting skills, live teaching hours, practice tasks, code reviews and total cost. Ask what you will finish and who will review it. A longer list of tools does not tell you how much practice you will get. Choose a plan that fits your current skills and available study time.

Are the project examples actual customer deployments?

These are learning exercises using synthetic data, which means made-up records, or sample data you have permission to use. They are not claimed as real client deployments. In your portfolio, clearly separate the project plan, the results you expected and the results you actually tested.

Sources and course information

Course information updated 25 October 2026. We provide the trainer, teaching hours, support and tuition details shown on this page. The external resources below explain engineering work and security concepts; they do not endorse or accredit our course.
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Other Relevant Courses

Generative AI Training Data Science Course in Hyderabad Prompt Engineering Course in Hyderabad Machine Learning Course in Hyderabad MLOps Course in Hyderabad

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