Brolly Academy | Agentic AI
Agentic AI Resume
Format, 30 Achievement Bullets and 3 Sample Resumes (2026)
How to write an agentic AI resume that gets shortlisted in 2026: what recruiters look for, the right structure, summary examples, the skills to list, 30 achievement bullets you can adapt, ATS keywords, and three sample resumes for freshers, developers and ML engineers.
Agentic AI resume guide for 2026: what recruiters look for, structure, summary examples, skills, 30 achievement bullets, ATS keywords and 3 sample resumes.
By Brolly Academy Team · Brolly Academy · Updated 23 October 2026
Table of Contents
1. What recruiters look for in an agentic AI resume
2. The right resume structure for AI engineer roles
3. Resume summary examples you can adapt
4. Skills section: what to list for agentic AI roles
5. 30 achievement bullets for agentic AI and LLM projects
6. How to show projects and GitHub
7. ATS keywords for agentic AI and AI engineer resumes
8. Sample resume 1: fresher with projects
9. Sample resume 2: software developer moving to AI
10. Sample resume 3: ML engineer moving to agents
11. Certifications, LinkedIn and salary signals
Quick answer
A strong agentic AI resume proves that you can build, test and ship AI agents, not just that you have "used ChatGPT". Recruiters want Python, LLM APIs, tool calling, RAG, at least one agent framework such as LangGraph or CrewAI, and signs that you think about evaluation, cost and safety. Show this through two to four real projects with GitHub links, bullets that say what the agent does and how you measured it, and a skills section that uses the exact words in the job post.
- Structure: header, summary, skills, projects, experience, education, certifications
- Must-have skills: Python, LLM APIs, tool calling, RAG, one framework, MCP basics, evaluation
- Proof: GitHub repos with a README, a demo, and honest numbers
- ATS: standard headings, one-column layout, keywords from the job post
- Length: one page for freshers, up to two pages for experienced engineers
Key takeaways
- On 23 October 2026, Naukri showed 11,031 results for the keyword "agentic ai" in India and 2,294 for Hyderabad, so recruiters are searching for these words.
- Projects are your strongest proof. One working agent with a clean README beats a long list of tool names.
- Write bullets as action + what you built + tool + result. If you have no real metric, measure your own project honestly.
- List current tools. Microsoft Agent Framework has replaced AutoGen, and MCP is now hosted under the Linux Foundation.
- Tailor every resume to the job post, using its exact skill words where they are true for you.
1. What recruiters look for in an agentic AI resume
Short answer: Recruiters look for proof that you can turn a large language model into a working agent that uses tools, retrieves data, follows rules and runs reliably, plus the software basics to put it into production.
A few terms first. An LLM (large language model) is the AI model, such as GPT, Claude or Gemini, that reads and writes text. Tool calling means the model asks your code to run a function, such as a search or database query, and uses the result. RAG (retrieval-augmented generation) means the agent looks up relevant documents before it answers. Orchestration means coordinating the steps, tools and agents in a workflow.
Anthropic's guide Building effective agents (Anthropic, December 2024) separates workflows, which follow predefined code paths, from agents, where the LLM directs its own process and tool use. Recruiters like candidates who know this difference, because it shows you will not build a fully autonomous agent when a simple workflow is enough.
Hiring managers in India usually check five things in about a minute:
- Can you code? Python, FastAPI, REST APIs, Git and basic SQL.
- Have you built an agent end to end? One agent that calls tools, keeps memory and handles errors beats ten tutorials.
- Do you know the current stack? LangGraph, CrewAI, OpenAI Agents SDK, Google ADK, Microsoft Agent Framework, Pydantic AI, LlamaIndex, MCP and a vector database.
- Do you think about quality and risk? Evaluation, guardrails, human approval for risky actions, logging and cost control.
- Can you explain impact? Bullets that say what changed because of your work.
OpenAI's A practical guide to building agents (OpenAI) describes an agent as a model, tools and instructions, and lists guardrails such as safety classifiers, PII filters, tool safeguards and output validation, plus human-in-the-loop checks for high-risk actions. If your resume touches all three parts and at least two guardrails, you already look better prepared than most applicants.
Demand is real but titles vary. On 23 October 2026, Naukri listed 11,031 results for "agentic ai" in India and 2,294 for Hyderabad and Secunderabad. These are keyword matches that change daily, not a count of jobs that require agentic AI. Titles seen include Agentic AI Engineer, Generative AI Engineer, Agentic AI Architect and AI/ML Engineer (GenAI and Agentic AI). For roles and where to apply, read our guide to agentic AI jobs.
2. The right resume structure for AI engineer roles
Short answer: Use a simple one-column layout with standard headings: header, summary, skills, projects, experience, education and certifications. Put experience above projects only if your work is directly relevant to AI.
An ATS (applicant tracking system) is the software companies use to store, filter and search resumes. It reads plain text best. Two-column templates, icons, skill bars and text inside images often get scrambled.
Keep it to one page if you are a fresher or have under three years of experience; experienced engineers can use up to two pages. Save it as a PDF unless the portal asks for Word, use a clean font such as Calibri or Arial at 10 to 12 points, write dates in one format, and name the file clearly, for example FirstName-LastName-AI-Engineer-Resume.pdf.
3. Resume summary examples you can adapt
Short answer: A good summary names the role you want, your core agent stack and your single best piece of proof, in two to four lines.
Skip phrases such as "passionate AI enthusiast". Replace them with facts. X and Y below are placeholders: fill them with real numbers or remove them.
Fresher, B.Tech CSE: Final-year computer science student building AI agents in Python. Built a LangGraph customer support agent with RAG over product documents and tool calling for order lookups, deployed with a public demo. Seeking an AI Engineer or Generative AI Engineer trainee role.
Fresher, non-CS graduate: B.Sc graduate who moved into AI engineering through self-study and a three-month agentic AI course. Built three working agents (document Q&A, email research, data analysis) with Python, the OpenAI API and CrewAI, each with a README and demo video on GitHub.
Software developer moving to AI: Backend developer with X years in Python and Java microservices, now building LLM agents. Added a tool-calling agent to an internal ticketing system using FastAPI and LangGraph, with human approval before any write action.
ML engineer moving to agents: Machine learning engineer with X years in model training and MLOps, now building agentic systems. Built multi-agent workflows with evaluation pipelines and tracing to monitor cost, latency and output quality.
Experienced AI engineer: AI engineer with X years building LLM applications, including Y production agents for support and operations teams. Strong in LangGraph, MCP, RAG and evaluation, with human-in-the-loop controls for high-risk actions.
4. Skills section: what to list for agentic AI roles
Short answer: Group skills into clear categories and list only current tools you can discuss in an interview.
Get the framework facts right
Interviewers notice outdated tools. As of October 2026:
- LangGraph is LangChain Inc.'s MIT-licensed framework for stateful, long-running agents built as graphs (LangChain docs, October 2026). LangChain is the broader library from the same company, so list both separately if you have used both.
- Microsoft Agent Framework is the direct successor to Semantic Kernel and AutoGen. AutoGen's GitHub repository says it is archived and no longer maintained, so list AutoGen only as legacy experience.
- Amazon Bedrock AgentCore is the AWS platform for production agents; Bedrock Agents Classic is closed to new customers.
- Flowise was archived in August 2026. For a low-code tool, n8n, Dify or Langflow are active options.
MCP (Model Context Protocol) is an open standard that lets an AI application connect to tools and data in one consistent way, like a common plug. Anthropic introduced it in November 2024 and donated it to the Agentic AI Foundation under the Linux Foundation in December 2025; the spec lives at modelcontextprotocol.io. A2A (Agent2Agent), created by Google in April 2025, lets agents from different vendors talk to each other. If you have built even a small MCP server, say so: it is still uncommon on fresher resumes.
Do not add a long soft-skills list. Show them inside bullets: "worked with the support team to define escalation rules" proves communication better than the word itself.
5. 30 achievement bullets for agentic AI and LLM projects
Short answer: Use the pattern action verb + what you built + tools + measured result. Replace every X, Y, Z and N with your own honest numbers, or remove them.
These are templates, not claims. In a personal project you can still measure real things: correct answers on a test set, time per task before and after a change, tokens per run, or steps that needed human review.
Building agents and tool calling
- Built a customer support agent in Python with LangGraph that checks order status through a REST API tool and answers from a product FAQ, tested on N sample tickets.
- Designed X tool functions with typed inputs and clear descriptions, cutting wrong tool calls on my test set from Y% to Z%.
- Added structured output with Pydantic models so every response is valid JSON, removing parsing errors downstream.
- Built an email and sales research agent that gathers public company information and drafts outreach emails for human review.
- Added retry and fallback logic for failed tool calls so the agent recovers from API timeouts.
RAG and memory
6. Built a document Q&A agent over N PDF policies using embeddings and ChromaDB, citing the source page in every answer.
7. Compared three chunking strategies and picked the one that raised retrieval accuracy on a X-question test set from Y% to Z%.
8. Added a reranking step that put the correct passage in the top three results for X% of test questions.
9. Added short-term conversation memory and a long-term preference store so the agent keeps context across sessions.
10. Built an agentic RAG flow where the agent decides when to search, rewrites vague queries and asks a clarifying question when results are weak.
MCP, integrations and automation
11. Built an MCP server exposing a knowledge base and a ticket tool, and connected it to an MCP-compatible client.
12. Connected an agent to Google Sheets, Gmail and Slack APIs to automate a weekly report that took X hours by hand.
13. Built an n8n workflow agent that classifies incoming requests and routes them, with human checks on low-confidence cases.
14. Wrapped a SQL database as a read-only tool with query validation that blocks write and delete statements.
15. Connected an agent to a calendar API to book and reschedule meetings, with confirmation before every change.
Multi-agent systems and orchestration
16. Built a three-agent CrewAI crew (researcher, writer, reviewer) where the reviewer sends back drafts with unsourced facts.
17. Designed a supervisor pattern in LangGraph that routes tasks to billing, technical and account specialist agents.
18. Cut average steps per task from X to Y by replacing a free-form multi-agent loop with a fixed workflow for predictable cases.
19. Built a resume screening agent that scores applications against a job description and explains each score, leaving decisions to a recruiter.
20. Built a data analysis agent that runs Python on uploaded CSV files in a sandbox and returns charts with a plain-English summary.
Evaluation, guardrails and safety
21. Created an evaluation set of N questions with expected answers and ran it on every code change to catch regressions.
22. Used an LLM-as-judge rubric for accuracy and tone, and hand-checked a sample to confirm it agreed with human review.
23. Added input guardrails for prompt injection and off-topic requests, and an output check that masks phone numbers and emails.
24. Added human-in-the-loop approval for refunds above a set value, logging what the agent proposed and what the human chose.
25. Set up tracing for every run to see each prompt, tool call and response, which helped fix X failure cases.
Deployment, cost and performance
26. Deployed the agent as a FastAPI service in Docker on AWS with environment-based secrets and a health-check endpoint.
27. Cut cost per task by X% by routing simple queries to a smaller model.
28. Reduced average response time from X to Y seconds with caching and parallel tool calls.
29. Added rate limits, timeouts and a maximum step count so a looping agent cannot run up a large bill.
30. Wrote a README with architecture diagram, setup steps, test results and known limits, plus a two-minute demo video.
Pick three or four bullets per project and two to six per job, strongest first. Include at least one evaluation or safety bullet, because most fresher resumes miss it.
6. How to show projects and GitHub
Short answer: Show two to four projects that solve real problems, each with a GitHub link, a clear README, a demo, and two to four bullets covering what the agent does, the stack and how you tested it.
Each project entry needs a name and one-line purpose ("SupportPilot: support agent for an online store"), the stack, GitHub and demo links, and your bullets. On GitHub, each README should explain:
- The problem and who would use the agent
- An architecture diagram of the LLM, tools, memory and data flow
- Setup steps a stranger can follow
- Evaluation results, such as test set size and accuracy
- Known limits and next improvements
Never commit API keys; use environment variables. Commit regularly with meaningful messages, pin your best three repositories, and keep course forks separate from your own work. Interviewers often open a file and ask you to explain it, so only show code you understand.
Choose projects close to the jobs you want: a support agent, a document Q&A agent, a workflow automation agent and a data analysis agent cover the most common business uses. For more ideas, see our list of AI agent projects.
7. ATS keywords for agentic AI and AI engineer resumes
Short answer: Use the exact keywords from each job post, in plain text, in both your skills section and your bullets.
Recruiters search ATS databases for words like "LangGraph" or "agentic AI". If those words are missing, you do not show up.
- Highlight every skill in the job post and add the ones you truly have.
- Write the full name and short form once, for example "Retrieval-Augmented Generation (RAG)".
- Put keywords inside bullets, not only in the skills list. Never hide them in white text.
8. Sample resume 1: fresher with projects
Short answer: A fresher resume leads with skills and projects, keeps education short, and fits on one page.
Header: Your Name · Hyderabad · Phone · Email · LinkedIn · GitHub · Demo link
Summary: Final-year B.Tech CSE student building AI agents with Python and LangGraph. Built a support agent with RAG and tool calling, a document Q&A agent with citations and a data analysis agent, each with a README, test set and demo on GitHub. Seeking an AI Engineer trainee role.
Skills
- Languages and tools: Python, SQL, Git, FastAPI, Docker
- LLMs and frameworks: OpenAI API, Gemini API, LangGraph, LangChain, CrewAI
- Retrieval and protocols: RAG, ChromaDB, FAISS, MCP (built one small server)
- Evaluation: test sets, LLM-as-judge, tracing, basic guardrails
Projects
SupportPilot, support agent (Python, LangGraph, OpenAI API, ChromaDB, FastAPI). GitHub and demo links.
- Built an agent that answers from a product FAQ and checks order status through a mock API tool.
- Improved correct answers on an N-question test set from X% to Y% by tuning chunk size and tool descriptions.
- Added human approval before refunds and an input check for off-topic requests.
DocAsk, document Q&A agent (Python, LlamaIndex, FAISS). GitHub link.
- Built a RAG agent over college regulation PDFs that cites the page and says "not found" when retrieval is weak.
DataBuddy, data analysis agent (Python, OpenAI Agents SDK, pandas). GitHub link.
- Built an agent that analyses uploaded CSV files in a sandbox and returns charts with a short summary.
Experience: Add an internship, hackathon, open-source contribution or freelance task if you have one; otherwise skip this section.
Education: B.Tech in Computer Science, college name, 2026.
Certifications: Hugging Face Agents Course certificate; agentic AI course completion certificate.
9. Sample resume 2: software developer moving to AI
Short answer: Keep experience first, rewrite bullets around APIs, automation and AI pilots, and add projects that prove agent skills.
Your production skills are your advantage, because many agent failures are software problems such as timeouts, bad inputs and missing logs.
Header: Your Name · Hyderabad · Phone · Email · LinkedIn · GitHub
Summary: Backend engineer with X years building Python and Java microservices, now building LLM agents. Added a tool-calling agent to an internal ticketing system with FastAPI and LangGraph, with human approval for every write action.
Skills
- Languages and backend: Python, Java, SQL, FastAPI, Spring Boot, PostgreSQL, Docker, Kubernetes
- AI: OpenAI and Claude APIs, LangGraph, Pydantic AI, RAG, MCP
- Cloud and quality: AWS, Bedrock AgentCore basics, GitHub Actions, tests, evaluation sets, tracing
Experience
Software Engineer, IT services company, 2022 to Present
- Built and maintained X REST microservices for a banking client.
- Led a pilot adding an LLM agent to the ticketing tool that drafts replies and fetches account data through read-only tools; support staff approve every reply.
- Wrote an evaluation set of N real-style tickets for the pilot, run in CI on every change.
- Reduced API error rates by X% with retries, timeouts and structured logging.
Projects
InvoiceAgent (Python, Pydantic AI, PostgreSQL). GitHub link.
- Built an agent that extracts invoice fields into typed models, validates totals and flags mismatches for review, with the database exposed through a read-only MCP server.
Education: B.E. in Information Technology, college name, 2021.
Certifications: AWS Certified AI Practitioner or AI-103 (if completed).
10. Sample resume 3: ML engineer moving to agents
Short answer: Present model evaluation and MLOps as strengths, then add agent projects that show orchestration, tools and monitoring.
Evaluation and monitoring are where many agent builders are weak, which is your edge.
Header: Your Name · Hyderabad · Phone · Email · LinkedIn · GitHub
Summary: Machine learning engineer with X years in model development and MLOps, now building multi-agent systems. Built evaluation and tracing pipelines for LLM agents to monitor quality, latency and cost per task.
Skills
- ML: Python, PyTorch, scikit-learn, model evaluation
- Agents: LangGraph, CrewAI, Google ADK, RAG, MCP, A2A concepts
- MLOps and cloud: MLflow, Docker, Kubernetes, tracing, Vertex AI, SageMaker, Pinecone
Experience
Machine Learning Engineer, product company, 2021 to Present
- Built and deployed X forecasting and classification models for operations teams.
- Designed a LangGraph prototype where a planner agent routes queries to retrieval, billing and escalation agents.
- Built an evaluation pipeline with N labelled queries and an LLM-as-judge rubric checked against human labels, used as a release gate.
- Added model routing to a smaller model for simple requests, cutting cost per task by X% in testing.
Projects
ResearchCrew (Python, CrewAI). GitHub link.
- Built a researcher, analyst and reviewer crew that writes sourced summaries and rejects unsourced claims.
Education: M.Tech in Computer Science, college name, 2020.
Certifications: NVIDIA-Certified Professional: Agentic AI (planned; registration showed "Coming soon" on 23 October 2026).
11. Certifications, LinkedIn and salary signals
Short answer: Vendor certifications help when they match the job, but projects matter more. Keep LinkedIn consistent with your resume.
Vendor options in October 2026 include the NVIDIA-Certified Professional: Agentic AI ($200, valid two years; registration showed "Coming soon" on 23 October 2026), the Microsoft AI-103 exam for Azure AI apps and agents, and the AWS Certified Generative AI Developer, Professional (300 USD). AI-102 is retired, so do not list it as a target. Our guide to agentic AI certifications compares them.
On LinkedIn, use the same titles, dates and project names as your resume, put your target role in the headline ("AI Engineer | LangGraph, RAG, MCP"), and add projects with demo links to the Featured section.
"Agentic AI engineer" is a new title, so salary samples are small. AmbitionBox showed a typical range of ₹11.4 to 14.8 lakh a year for Agentic AI Engineer (0 to 9 years, only 94 salaries, October 2026) and ₹15 to 16.6 lakh for AI Engineer (1 to 6 years, October 2026). See our agentic AI engineer salary guide.
12. Common agentic AI resume mistakes to avoid
Short answer: Avoid listing tools without proof, inventing numbers, using outdated frameworks, hiding GitHub and sending one generic resume everywhere.
- Tool soup: twenty frameworks and no project that uses them.
- Invented metrics: interviewers will ask how you measured them.
- A chat wrapper called an agent: show tools, memory, decisions and tests.
- Outdated stack: AutoGen, Flowise or Bedrock Agents Classic as your main skill.
- Secrets on GitHub: committed API keys are an instant red flag.
- One resume for all jobs: keep a master resume with every project and bullet, then tailor a copy for each application.
- Trusting an AI resume builder blindly: AI tools often add generic phrases, invented numbers or skills you lack. Check every line.
Once shortlisted, interviewers often pick a resume bullet and ask you to explain it, so prepare with our agentic AI interview questions.
13. How Brolly Academy helps
Short answer: Brolly Academy's three-month Agentic AI course helps you build the projects a strong resume needs, with resume and LinkedIn help, mock interviews and placement assistance.
The Agentic AI Course in Hyderabad covers foundations, then tools and workflows, then projects and deployment, plus career preparation, in classroom and online modes with weekend batches.
- Six capstone projects: customer support agent, document Q&A and RAG agent, workflow automation agent, email and sales research agent, resume screening and HR agent, and data analysis agent.
- Current topics: tool calling, MCP basics, agentic RAG, human-in-the-loop and evaluation, with LangGraph, CrewAI, LlamaIndex, OpenAI Agents SDK and Google ADK.
- Career support: resume and LinkedIn help, mock interviews, placement assistance and internship opportunities.
- Trainer: Mr. Dinesh, Agentic AI, Generative AI Specialist and Cloud Architect, 12+ years in AI, ML, cloud and Generative AI.
No AI experience is needed and Python is optional. Fees are ₹9,999 (video recordings), ₹24,999 (live online) and ₹29,999 (classroom), with easy EMI. The next batch starts Monday, 2 November 2026, and a free demo class is available. You receive a Brolly Academy course completion certificate, which is not a vendor certification.
Build the projects your agentic AI resume needs
Join Brolly Academy's three-month Agentic AI course in Hyderabad or online, build six capstone agents for your GitHub, and get resume, LinkedIn and mock interview support. Book a free demo class or call +91 81868 44555.
Frequently asked questions
What is an agentic AI resume?
An agentic AI resume is a resume written for roles that build AI agents, such as AI Engineer, Agentic AI Engineer or Generative AI Engineer. It highlights Python, LLM APIs, tool calling, RAG, agent frameworks like LangGraph or CrewAI, and evaluation skills. Most importantly, it proves those skills with working projects, GitHub links and clear achievement bullets rather than a plain list of tool names.
How do I write an AI engineer resume?
Start with a one-column layout and standard headings: header, summary, skills, projects, experience, education and certifications. Write a two to four line summary naming your target role, stack and best proof. Group your skills by category. Add two to four projects with GitHub links, and write each bullet as action plus what you built plus tool plus result. Finally, tailor keywords to each job post.
What skills should I put on an agentic AI resume?
List Python, SQL, Git and FastAPI as basics, then LLM APIs, prompt design and structured output. Add one or more agent frameworks such as LangGraph, CrewAI, OpenAI Agents SDK, Google ADK or Microsoft Agent Framework. Include RAG and a vector database, MCP, a cloud platform, and evaluation and guardrail skills. Only list skills you can explain confidently in an interview.
How do I write an agentic AI resume as a fresher with no experience?
Put projects above experience and keep the resume to one page. Build two to four agents that solve real problems, such as a support agent or a document Q&A agent, and publish each on GitHub with a README and demo. Measure your own results with a small test set. Add hackathons, open-source work or internships if you have them, and keep education short.
What projects should I add to an agentic AI resume?
Choose projects that match common business uses: a customer support agent with tool calling, a document Q&A agent using RAG, a workflow automation agent, a multi-agent research crew or a data analysis agent. Each should include a clear purpose, stack, GitHub link, demo and at least one bullet about testing or guardrails. Projects close to your target jobs work best.
How many projects should an agentic AI resume have?
Two to four projects is the right number for most resumes. Freshers should aim for three strong ones, each with three or four bullets. Experienced engineers can show one or two projects alongside work experience. Quality beats quantity, so one well-tested agent with a clean README is worth more than many unfinished tutorial copies.
Should I include GitHub on my AI engineer resume?
Yes, always include GitHub, and put it in the header next to your email and LinkedIn. Recruiters treat it as evidence for your claims. Pin your best three repositories, write clear READMEs with setup steps and test results, commit regularly, and never leave API keys in the code. Interviewers often open a file and ask you to explain it.
What are good ATS keywords for an agentic AI resume?
Useful keywords include AI Engineer, agentic AI, AI agents, LLM, generative AI, RAG, tool calling, multi-agent systems, LangGraph, LangChain, CrewAI, MCP, vector database, embeddings, FastAPI, Docker, AWS, Azure, evaluation, guardrails and human-in-the-loop. Always copy the exact words from each job post, as long as they are true for you, and use them in bullets as well as the skills list.
How long should an AI engineer resume be?
Keep it to one page if you are a fresher or have under three years of experience. Experienced engineers can use up to two pages. Recruiters skim quickly, so remove school details, unrelated hobbies and tools you used only once. Every line should support the role you are applying for.
What should I write in an AI engineer resume summary?
Write two to four lines covering the role you want, your core agent stack and your single best piece of proof. For example, mention that you built and deployed a LangGraph support agent with RAG and tool calling. Avoid empty phrases such as passionate AI enthusiast. Facts and named projects make a summary stand out.
Should I list LangChain and LangGraph separately on my resume?
Yes, list them separately if you have used both. LangChain is the broader agent engineering library, while LangGraph is LangChain Inc.'s framework for stateful, long-running agents built as graphs. Recruiters and ATS tools often search for each name on its own, so listing both helps, but only if you can explain what you built with each.
Is AutoGen still worth putting on my resume?
Only as past experience. Microsoft's AutoGen repository now says the project is archived and no longer maintained, and Microsoft Agent Framework is its direct successor. If you built something with AutoGen, keep it, but show that you know the current tool, ideally with a small project in Microsoft Agent Framework or another active framework.
Should I mention MCP on my resume?
Yes, if you have used it. MCP, the Model Context Protocol, is an open standard for connecting AI applications to tools and data. Anthropic introduced it in November 2024 and donated it to the Agentic AI Foundation under the Linux Foundation in December 2025. Building even a small MCP server is still uncommon on fresher resumes and helps you stand out.
How do I show results on my resume without real company metrics?
Measure your own projects honestly. Build a small test set and report how many questions the agent answered correctly, how long tasks took before and after a change, tokens used per run, or how many steps needed human review. Never invent numbers you did not measure, because interviewers will ask how you got them.
Should I put certifications on an agentic AI resume?
Yes, add relevant certifications in their own section with the issuer and year, but remember projects matter more. Vendor options include the NVIDIA-Certified Professional: Agentic AI, the Microsoft AI-103 exam for Azure AI apps and agents, and the AWS Certified Generative AI Developer, Professional. Do not list AI-102 as a current goal, because it is retired.
How do I move from software developer to AI engineer on my resume?
Keep experience first and rewrite bullets to highlight APIs, automation, testing and any AI pilots. Add a skills group for LLMs, agent frameworks, RAG and MCP. Then add one or two agent projects that use your backend strengths, such as a tool-calling agent behind a FastAPI service with logging and human approval for write actions.
How do I present ML experience for agentic AI roles?
Frame your model evaluation and MLOps work as strengths, because evaluation and monitoring are where many agent builders are weak. Add bullets about evaluation pipelines, tracing, model routing and cost control. Then show at least one agent project that uses orchestration and tools, such as a LangGraph or CrewAI multi-agent workflow with a test set.
What is the best resume format for AI jobs in India?
A simple one-column PDF with standard headings works best for Indian job portals and company ATS tools. Use a clear font at 10 to 12 points, consistent date formats and no graphics, skill bars or text inside images. Put your city, phone, email, LinkedIn and GitHub in the header, and name the file with your name and target role.
Can I use an AI resume builder for an agentic AI resume?
You can use AI tools to draft or polish wording, but check every line yourself. AI tools often add generic phrases, invented numbers or skills you do not have. Make sure the final resume uses a plain ATS-friendly layout, contains only true claims and keeps your own project details, links and measured results.
Do I need a different resume for each AI job?
Yes, tailor it for each role. Keep one master resume with all your projects and bullets, then for each application reorder bullets, adjust the summary and copy the exact keywords from the job post. A support-agent role and an AI platform role need different emphasis, even when your skills are the same.
What should a generative AI engineer resume include?
A generative AI engineer resume should include Python, LLM APIs, prompt design, structured output, RAG with a vector database, and at least one framework such as LangChain or LlamaIndex. Adding agent skills like tool calling, LangGraph and MCP widens the roles you fit. Back each skill with a project, a GitHub link and a measured result.
How do I add agentic AI skills to my LinkedIn profile?
Use the same job titles, dates and project names as your resume so everything matches. Put your target role and key skills in the headline, for example AI Engineer with LangGraph, RAG and MCP. Add your agent projects with demo links to the Featured section, and list your core frameworks and protocols in the Skills section.
How much does an agentic AI engineer earn in India?
AmbitionBox showed a typical range of 11.4 to 14.8 lakh rupees a year for Agentic AI Engineer in October 2026, based on only 94 salaries across 0 to 9 years of experience. For AI Engineer, with a much larger sample, the typical range was 15 to 16.6 lakh for 1 to 6 years. Treat these as rough ranges, not offers.
Are there many agentic AI jobs in Hyderabad?
Interest is strong. On 23 October 2026, Naukri showed 2,294 results for the keyword agentic ai in Hyderabad and Secunderabad, and 11,031 across India. These are keyword matches that change daily, not a count of jobs that strictly require agentic AI, but they show recruiters are searching for these skills on resumes.
Does Brolly Academy help with agentic AI resumes?
Yes. Brolly Academy's three-month Agentic AI course includes resume and LinkedIn help, mock interviews, placement assistance and internship opportunities. Students also build six capstone projects, including a support agent, a RAG agent and a data analysis agent, which give them real projects and GitHub repositories to show on their resumes.
Learn with Brolly Academy
Review the syllabus, practical projects and batch timings, and book a free demo before you enrol.
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Microsoft Foundry, Azure OpenAI, RAG, agents and evaluation, for building AI apps and agents on Azure.
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