Generative AI Resume

Quick answer

A strong generative AI resume shows, in one or two pages, that you can build working LLM applications: it leads with a short summary, lists GenAI skills that match the job description, and proves them with 2 to 4 projects or work bullets that name the tool, the task and a measured result. Recruiters in India scan for Python, LLM APIs, RAG, LangChain or LlamaIndex, vector databases, prompt engineering, evaluation and one cloud platform. Of the 12 GenAI job descriptions we read on Naukri and LinkedIn on 24 October 2026, all asked for Python and 11 asked for RAG. Your resume must use those words and back them with evidence, such as a GitHub link or a deployed demo.

Key takeaways

1. Generative AI resume at a glance

Short answer: A generative AI resume follows the normal resume order, but the skills, projects and bullets are written around LLM application work, and the emphasis changes with your experience level.

Use this table to see what each profile should put first. It is the plan the four sample resumes later in this post follow.

Note: "generative AI resume" can also mean using AI to write or screen resumes. This post is about the resume you need to get a GenAI job. Using an AI assistant to polish wording is fine if every claim is true and you can explain it in an interview.

2. What recruiters look for in a generative AI resume

Short answer: Recruiters look for proof that you can build, evaluate and deploy LLM applications, written in the same words the job description uses, with Python, RAG, frameworks, vector databases and cloud at the top of the list.

We read 12 Generative AI job descriptions on Naukri and LinkedIn on 24 October 2026, from Capgemini, Infosys, Tech Mahindra, PwC, Tiger Analytics, Coforge, Wells Fargo, TCS, JPMorganChase, TVS Credit, Crisil and a recruiter hiring for a client. These are listings seen on that date, not hiring commitments. The themes below are what your resume must answer.

Quick glossary: an LLM (large language model) predicts the next token, a word or part of a word. RAG retrieves chunks of your own documents and passes them to the LLM so answers stay grounded. An embedding is a list of numbers that captures meaning, stored in a vector database such as FAISS or Chroma. A hallucination is a confident false answer. An agent plans steps and calls tools. Fine-tuning trains a model further on your data; LoRA does it cheaply by training small added matrices.

Three more patterns shape how you write:

For a fuller view of roles, cities and companies, read our guide to Generative AI jobs in India. On 24 October 2026, Naukri showed about 10,953 results for "generative ai" across India, about 7,585 for "rag" and about 4,323 for "langchain" (Naukri, October 2026). These are keyword-match counts, not a count of open GenAI jobs, but they tell you which words recruiters search for.

3. Generative AI resume structure, section by section

Short answer: Use seven sections in this order: header, summary, skills, projects (or experience first if you have relevant GenAI work), experience, education and certifications, each written for the specific job you are applying to.

Header

Summary (3 to 4 lines)

Use this formula: role + years + what you build + key tools + one proof point. The four samples in section 4 each show one.

Skills

Group skills so a recruiter can scan them in five seconds. Use the same names the JD uses.

Projects, experience, education, certifications

Formatting rules that keep you ATS-safe

An ATS (applicant tracking system) is the software companies use to store, parse and search resumes. It reads text, not design.

4. Four sample generative AI resumes

Short answer: Below are four fictional sample resumes, for a fresher, a Python developer moving to GenAI, a GenAI engineer with 3 to 5 years and a senior AI or LLM architect; copy the structure, then replace every detail and number with your own.

All four samples are fictional. The names, companies and numbers are invented to show format. Do not copy the metrics; measure your own.

Sample 1: Fresher generative AI resume (AI engineer resume for freshers)

Ananya Reddy · Hyderabad · ananya.reddy@email.com · linkedin.com/in/ananya-reddy · github.com/ananya-r
Target role: Generative AI Engineer / AI Engineer (fresher)

Summary: B.Tech (CSE) 2026 graduate who builds LLM applications in Python. Built three GenAI projects covering RAG, prompt engineering and a simple tool-using agent. Completed a 3-month GenAI internship. Comfortable with LangChain, Chroma, Hugging Face and the Gemini and GPT APIs.

Skills:

Projects:

Experience:

Education: B.Tech, Computer Science and Engineering, 2026, CGPA 8.1

Certifications: AWS Certified AI Practitioner (2026)

Sample 2: Gen AI developer resume for a Python developer moving to GenAI (2 to 4 years)

Rahul Sharma · Pune · rahul.sharma@email.com · linkedin.com/in/rahulsharma-dev · github.com/rahuls-dev
Target role: Gen AI Developer / Generative AI Engineer

Summary: Python backend developer with 3 years of experience building REST APIs and data pipelines at an IT services company. For the last 8 months, built GenAI features for an internal client project, including a RAG search service on Amazon Bedrock. Strong in Python, SQL, FastAPI, Docker and AWS.

Skills:

Experience:

Projects:

Education: B.E., Information Technology, 2023

Sample 3: Generative AI engineer resume (3 to 5 years)

Priya Nair · Bengaluru · priya.nair@email.com · linkedin.com/in/priyanair-ai · github.com/priyanair
Target role: Senior Generative AI Engineer / LLM Engineer

Summary: Generative AI engineer with 5 years in ML and software, including 3 years shipping LLM applications for banking and insurance clients at a global capability centre. Built production RAG and agent systems on Microsoft Foundry and Azure OpenAI, with evaluation pipelines, guardrails and cost tracking.

Skills:

Experience:

Education: M.Tech, Computer Science, 2021

Certifications: Microsoft Certified: Azure AI Apps and Agents Developer Associate (2026)

Sample 4: Senior AI / LLM architect resume (8+ years)

Karthik Iyer · Chennai · karthik.iyer@email.com · linkedin.com/in/karthik-iyer-ai
Target role: AI Architect / LLM Architect / Head of GenAI Engineering

Summary: AI architect with 12 years in software and data, the last 4 leading enterprise GenAI programs. Designed a shared LLM platform used by 14 product teams, set up responsible AI and security reviews, and moved 9 use cases from pilot to production across AWS and Azure.

Core strengths:

Experience:

Education: B.Tech, Electronics and Communication, 2014

5. 40 generative AI resume bullet examples by area

Short answer: Here are 40 bullets in eight groups (RAG, fine-tuning, prompt engineering, agents, evaluation, deployment and LLMOps, data pipelines and cloud AI services); adapt the wording, keep the action + tool + result pattern, and replace the numbers with ones you measured.

The numbers in these bullets are illustrative. Use them as a pattern, not as claims.

RAG systems

Fine-tuning

6. Fine-tuned an open 7B model with LoRA using Hugging Face PEFT on 5,000 labelled support tickets, raising classification accuracy from 78% to 91%.
7. Used QLoRA to fine-tune on a single GPU, keeping training cost within a small project budget.
8. Built and cleaned an instruction dataset of 3,000 examples, removing duplicates and personal data before training.
9. Compared a fine-tuned small model with a prompted large model on cost, latency and accuracy, and documented when each should be used.
10. Set up model versioning in MLflow so every fine-tuned checkpoint could be traced to its data and settings.

Prompt engineering

11. Wrote and versioned 60 prompt templates with few-shot examples for a support chatbot, cutting off-topic answers by 45%.
12. Designed structured JSON output prompts with schema validation, reducing parsing failures to under 1% of calls.
13. Built a prompt test harness that runs 200 test cases on every prompt change before release.
14. Reduced average prompt length by 35% without losing accuracy, lowering token cost per request.
15. Added system-level instructions and refusal rules that blocked out-of-scope financial advice in a banking assistant.

Agents and tool use

16. Built a LangGraph agent that queries a SQL database, summarises results and asks for human approval before sending reports.
17. Integrated 5 internal APIs as tools through MCP, letting an assistant check order status, refunds and delivery dates.
18. Designed a multi-agent workflow with CrewAI (researcher, writer, reviewer) that drafts first-pass market summaries for analysts.
19. Added step limits, timeouts and fallbacks to an agent, cutting failed or looping runs by 60%.
20. Logged every agent step and tool call for audit, meeting the client's compliance review requirements.

Evaluation

21. Created a 500-question golden test set with domain experts and ran it on every release to catch regressions.
22. Measured groundedness and hallucination rates with LLM-as-judge plus human spot checks, raising groundedness from 0.74 to 0.90.
23. Built a dashboard tracking answer quality, latency and cost per query across three model options.
24. Ran A/B tests between two prompt versions with 1,200 real user queries and shipped the version with higher user ratings.
25. Added red-team tests for prompt injection and data leakage, closing 12 issues before go-live.

Deployment and LLMOps

26. Deployed a RAG service as a FastAPI app in Docker on Kubernetes, serving 50,000 queries a month.
27. Set up CI/CD that runs unit tests, prompt tests and evaluation checks before each deployment.
28. Added LangSmith tracing to debug slow chains, cutting p95 latency from 6 seconds to 2.5 seconds.
29. Introduced response caching and model routing, reducing monthly LLM spend by 38%.
30. Built monitoring alerts for error rates, token usage spikes and drops in evaluation scores.

Data pipelines

31. Built a document pipeline that extracts text and tables from scanned PDFs with OCR and cleans it for embedding.
32. Wrote Python ETL jobs that pull data from SharePoint and Confluence nightly into a vector index.
33. Masked personal data (names, phone numbers, PAN) before documents reached the LLM.
34. Deduplicated 40,000 documents, shrinking the index by 25% and improving retrieval precision.
35. Designed SQL tables to store prompts, responses, user feedback and evaluation scores for analysis.

Cloud AI services

36. Built a claims-summary assistant on Amazon Bedrock with Lambda and S3, processing 2,000 claims a week.
37. Deployed GPT models through Azure OpenAI in Microsoft Foundry with private networking and role-based access.
38. Prototyped a document assistant on Gemini Enterprise Agent Platform (formerly Vertex AI) using managed vector search.
39. Used SageMaker to host a fine-tuned open model behind an internal API.
40. Compared Bedrock and Azure OpenAI on cost, latency and data controls for a client and wrote the recommendation adopted by the steering group.

Weak bullet, strong bullet: here is the change in one line.

6. Skills section by level and ATS keyword list

Short answer: List skills that match your level honestly, grouped by category, and include the exact keywords Indian GenAI JDs use, such as Python, LLM, RAG, LangChain, LangGraph, vector database, embeddings, prompt engineering, fine-tuning, evaluation and your cloud platform.

Skills by level

ATS keyword list from real JDs

These keywords appeared in the GenAI job descriptions we read on Naukri and LinkedIn on 24 October 2026. Use only the ones you can back up.

Two quick tips. First, write both forms of key terms once, such as "RAG (retrieval-augmented generation)" and "LLM (large language model)", because recruiters search either form. Second, Microsoft now calls Azure AI Foundry "Microsoft Foundry", and Google moved Vertex AI into Gemini Enterprise Agent Platform on 22 April 2026. JDs still use the old names, so write "Microsoft Foundry (Azure AI Foundry)" or "Gemini Enterprise Agent Platform (Vertex AI)" to match both.

7. Projects section: generative AI projects for resume and GitHub links

Short answer: Pick 2 to 4 projects that mirror the job description, host each on GitHub with a clear README and a demo, and describe each on your resume with the problem, the stack and a measured result.

Projects are the strongest proof on a fresher's generative AI resume. One 0 to 2 year listing we read on 24 October 2026 said internships and projects count as experience, and TVS Credit's listing said strong project portfolios from fresh candidates may be considered. For more ideas, see our list of generative AI projects for beginners.

Five projects that match what JDs ask for

GitHub checklist for every project

On the resume, use three lines per project: title and link, stack, and two measured results, as in Sample 1. Your LinkedIn profile should match: same title, same project names and the same skills, so a recruiter who checks both sees one clear story.

8. Certifications section

Short answer: Add one or two relevant certifications in a short section near the end; they can support a switch or a fresher profile, but projects and work results matter more, and only one of the 12 JDs we read mentioned certifications at all.

PwC's GenAI data scientist JD listed "relevant AI/GenAI certifications preferred". No other JD we read on 24 October 2026 asked for one. So use certifications as supporting proof, not as your main selling point. Current options that fit a GenAI resume include:

You can check the details on the Google Cloud Generative AI Leader page, the AWS Certified AI Practitioner page and the Microsoft AI-103 certification page (all checked October 2026). Note that Microsoft's older Azure AI Engineer Associate (AI-102) is retired, so do not list it as a target. If you hold it already, keep it with its year. Our Generative AI certification guide compares the full list, including AWS, NVIDIA and Databricks options.

How to write it:

9. Rewriting your resume when you switch into GenAI

Short answer: Keep your past roles, but rewrite the bullets to highlight the skills GenAI teams need (Python, APIs, testing, data, domain knowledge), add any GenAI work you did inside your job, and back it with two strong side projects.

Many people moving into GenAI in India come from IT services and GCC roles in Hyderabad, Bengaluru, Pune and Chennai. Here is how each background should reframe.

Rewrite example for a tester:

Be honest about timelines. Put side projects in a "Projects" section with dates, and do not stretch them into job experience.

10. Common generative AI resume mistakes

Short answer: The most common mistakes are vague bullets, keyword stuffing without proof, tool lists with no projects, no links, and claims you cannot explain in an interview.

11. Short cover letter sample

Short answer: Keep a GenAI cover letter to about 150 words: the role, one project or result that matches the JD, why this company, and a call to talk.

Many portals do not ask for one, but a short note helps for direct emails, referrals and startup roles. Here is a fictional sample.

Dear Hiring Manager,

I am applying for the Generative AI Engineer role in Hyderabad. I am a 2026 B.Tech (CSE) graduate who builds LLM applications in Python.

Your JD asks for RAG, LangChain and vector databases. My CampusDocs project is a RAG assistant over 120 policy PDFs, built with LangChain, Chroma and the Gemini API. I created a 150-question test set and raised correct answers from 64% to 83% by tuning chunking and adding reranking. The code and demo are on my GitHub.

During my internship, I wrote and tested 40 prompt templates for a support chatbot and built a simple evaluation sheet that caught wrong answers before release.

I would value a chance to discuss how I can help your team ship reliable GenAI features.

Regards,
Ananya Reddy

For where to send it, our guides on Generative AI jobs for freshers and how to become a Generative AI engineer cover entry titles, portals and the skills path.

12. What your resume is worth: a quick pay reality check

Short answer: Know the range for your target title before you apply, so your resume aims at the right level; AmbitionBox showed a typical ₹9.2 to 10.1 lakh a year for Generative AI Engineers in India in October 2026.

AmbitionBox showed a typical range of ₹9.2 to 10.1 lakh a year for Generative AI Engineers in India, based on 632 salaries covering 0 to 13 years of experience, updated 23 October 2026. It is a typical range across all levels, not an offer. Freshers usually start lower, and engineers who show live RAG or agent systems, evaluation and cloud deployment tend to sit higher. On Naukri, "generative ai" filtered to 0 years' experience returned 284 results on 24 October 2026, many of them internships and some clearly senior roles mis-tagged (Naukri, October 2026).

13. How Brolly Academy helps

Short answer: Brolly Academy's 3-month Generative AI course gives you the projects, skills and resume support to build a credible GenAI resume, in classroom, live online or self-paced video formats.

The Generative AI Training in Hyderabad course runs for 3 months across 16 syllabus modules, covering prompt engineering, LLMs, embeddings, chunking, RAG and advanced RAG with LangChain, vector databases (Chroma DB, Weaviate, FAISS, Qdrant, Pinecone), fine-tuning with LoRA and QLoRA, LLM evaluation, LLMOps, and LLMs on cloud with Amazon Bedrock and Azure OpenAI (Brolly course page, October 2026). That maps closely to the ATS keywords in this post.

What helps your resume directly:

You get a Brolly Academy course completion certificate after the course and capstone. No vendor certification is included.

Frequently asked questions