5 Generative AI Projects to Add to Your Resume (2026)

A strong GenAI portfolio is the single highest-leverage differentiator for Pune AI / GenAI Engineer interview calls. The fresher AI / GenAI Engineer pay band in Pune in 2026 is ₹6-12 LPA, and the candidates who consistently land top-band offers are the ones with 3-5 production-quality GenAI projects rather than dozens of tutorial clones. This guide breaks down 5 GenAI project ideas you can actually add to your resume, with the specific architecture, scope, and tactics that make each strong for Pune hiring panels.

The headline pattern: deploy + document + measure. Projects that exist only as Jupyter notebooks don't move recruiters; projects that are deployed, documented with technical writeups, and measured for quality consistently do.

Project 1: Domain-specific RAG application

Build a Retrieval-Augmented Generation (RAG) application for a specific Pune-local or India-relevant domain.

Strong examples

• Pune restaurant menu Q&A bot — scrape menus from a few Pune restaurants, index them, answer "vegetarian Punjabi options near Kothrud under ₹400"
• Indian railway booking assistant — index IRCTC route data + ticket pricing rules, answer route + timing + fare queries
• Pune college admissions Q&A — index COEP / PICT / VIT Pune / MIT-WPU admission criteria + cut-offs, answer "what JEE score do I need for PICT CSE?"
• Indian Income Tax filing assistant — index Income Tax Act sections relevant to salaried professionals, answer common filing questions with citations

Stack to use

• LLM: GPT-4 or Claude 3.5 Sonnet (API)
• Framework: LangChain or LlamaIndex
• Vector DB: Pinecone, Weaviate, or pgvector (PostgreSQL extension)
• Embeddings: OpenAI ada-002 or open-source alternatives (BAAI/bge-small-en-v1.5)
• Frontend: Streamlit or Next.js
• Deployment: Vercel, Streamlit Cloud, or your own VPS

What makes it strong for Pune hiring

• Pune-local domain shows real-world thinking
• RAG is the most-screened production AI architecture at Pune product captives
• Citation requirements demonstrate awareness of hallucination prevention
• Deployment makes it tangible vs notebook-only

Build time

4-6 weeks for a polished deployment with documentation.

Project 2: Agentic AI workflow

Build a multi-step agent that uses tools to complete real tasks.

Strong examples

• Pune restaurant ordering agent — searches menus, picks based on preferences + budget + dietary restrictions, fills order forms
• Job search agent — scrapes Naukri / LinkedIn / company portals, filters by Pune location + stack, generates personalised cover letters
• Data analyst agent — given a CSV file, picks the right analysis approach, generates charts, writes summary report
• Travel planning agent — researches Pune-to-X flight + hotel + sightseeing, builds itinerary with budget constraints

Stack to use

• Framework: LangGraph (most flexible) or OpenAI Assistants API or Claude tool use
• Tools: function-calling for external API integrations
• LLM: GPT-4 or Claude (best at tool-use)
• State management: depends on agent complexity (Redis for sessions, DB for long-term)

What makes it strong for Pune hiring

• Agentic AI is the fastest-emerging Pune AI hiring track in 2026
• Demonstrates planning + tool-use + reasoning skills
• Shows you can build beyond simple LLM calls

Build time

5-8 weeks for a polished agent with multiple tools + clear UX.

See our Agentic AI track for the curriculum.

Project 3: Production LLM evaluation suite

Build an evaluation suite for an LLM-powered system you've built or chosen.

Strong examples

• Chatbot evaluation harness — runs 100+ test conversations, scores accuracy + helpfulness + safety
• Prompt regression testing tool — detects when prompt changes break previously-working tests
• RAG retrieval quality measurement — measures retrieval precision/recall on a benchmark dataset
• Hallucination detection pipeline — automated detection of unsupported claims in LLM responses

Stack to use

• Python + pytest for evaluation framework
• LangSmith or Helicone for observability
• Custom metrics specific to your application
• CI integration for automated evaluation on prompt changes

What makes it strong for Pune hiring

• Production AI work increasingly emphasises evaluation rigour
• Strong differentiator for senior AI Engineer track applications
• Shows engineering depth beyond surface-level LLM usage

Build time

3-4 weeks for a meaningful evaluation suite with documentation.

Project 4: Fine-tuned domain-specific LLM

Fine-tune an open-source LLM (Llama 3, Mistral, Phi-3) on a specific dataset.

Strong examples

• Pune real-estate listing classifier — fine-tune Mistral on Magicbricks data to classify listings (1BHK / 2BHK / 3BHK + locality + price band)
• Customer support response generator — fine-tune Phi-3 on customer support tickets + responses for a specific domain
• Code completion model for Spring Boot — fine-tune on Spring Boot codebase patterns
• Indian legal document summariser — fine-tune on Income Tax Act + Companies Act + court judgements

Stack to use

• Python + Hugging Face Transformers
• Unsloth or PEFT + LoRA for efficient fine-tuning
• Weights & Biases for experiment tracking
• vLLM or Text Generation Inference for deployment

What makes it strong for Pune hiring

• Shows ML engineering depth beyond prompt engineering
• Demonstrates understanding of when fine-tuning vs prompting wins
• Strong signal for ML Engineer track applications

Build time

6-8 weeks (data preparation + fine-tuning experiments + deployment).

Project 5: Multi-modal AI application

Build an application that combines text + image + audio.

Strong examples

• Pune market analysis from photos — upload a photo of a market scene, identify products + estimate prices
• Document understanding assistant — extract structured data from invoices, contracts, ID cards (using vision LLMs)
• Voice-based customer support — voice input → LLM processing → voice output
• Photo-to-recipe assistant — upload food photo, AI generates recipe + nutritional info

Stack to use

• Vision LLM: GPT-4V or Claude 3.5 Sonnet (vision)
• Audio: OpenAI Whisper (speech-to-text) + text-to-speech tools
• Standard LLM stack for the text generation layer
• Streamlit + custom Python for the multi-modal UX

What makes it strong for Pune hiring

• Multi-modal AI is the modern frontier
• Demonstrates you're tracking the latest AI capabilities
• Differentiator at top product captives

Build time

4-6 weeks for a polished multi-modal application.

How to structure each project for maximum portfolio impact

For every project on your resume:

1. Clean GitHub repository

• README with: problem statement, architecture diagram, demo screenshots / video, "what I'd improve next", how to run
• Clean Git history — meaningful commit messages
• Documentation in code — comments explaining non-obvious choices
• Tests — even basic test coverage signals production thinking

2. Working deployment

Hosted somewhere accessible — Vercel, Streamlit Cloud, Hugging Face Spaces, or your VPS. Recruiters need to see it working in 30 seconds.

3. Technical writeup

Either as a blog post (Medium, Hashnode, dev.to) or as a detailed README section. Cover:

• What problem you're solving
• Why you picked this architecture
• What you tried that didn't work
• What you'd change with more time

4. Demo video (60-90 seconds)

Loom or YouTube unlisted. Walks through the working demo with voiceover explaining technical choices.

5. Measurement / evaluation

Build basic quality measurement for the project. "It works for me" doesn't differentiate; "accuracy is X on Y benchmark" does.

How to pick the right project for your career stage

Career stage	Best project category
Fresher (no work experience)	Project 1 (Domain RAG) — accessible, broad coverage
Mid-level (2-3 years)	Project 2 (Agentic AI) or Project 4 (Fine-tuning) — depth signal
Career switcher	Project 1 + Project 3 — RAG + evaluation rigour shows engineering thinking
Senior (5+ years)	Project 3 (Evaluation) + Project 4 (Fine-tuning) — production AI depth
AI / GenAI specialisation focus	Project 5 (Multi-modal) — frontier work signal

Frequently asked questions

How many AI projects should I have on my resume? 3-5 deep projects beats 10 shallow ones. Aim for 3 high-quality projects with clean code, working demos, and writeups.

Should I build all 5 project categories? No — pick 3 that fit your career target. Generalist breadth isn't as strong as depth.

How long does each project take? 3-8 weeks of focused work depending on complexity. Build one strong project before starting the next.

Do I need to fine-tune to be hired for AI roles? No — RAG + prompt engineering + agentic AI projects are sufficient for most AI Engineer roles. Fine-tuning differentiates for ML Engineer specialisation.

Where can I host my deployed projects? Vercel (free tier), Streamlit Cloud (free tier), Hugging Face Spaces (free), or your own VPS. Free tiers work for portfolio purposes.

What's the typical Pune AI Engineer offer with a strong portfolio? ₹6-12 LPA fresher / ₹14-26 LPA mid-level. Strong portfolio is the primary differentiator within these bands. See Pune IT Salary Guide 2026.

Where can I learn the stack for these projects? Our Generative AI track covers LLMs, LangChain, RAG, fine-tuning, and deployment. Agentic AI track adds multi-agent patterns.

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For broader portfolio-building advice, see How to Build an AI Portfolio that Gets Interview Calls. For prompt engineering fundamentals, see 8 Common Prompt Engineering Mistakes Beginners Make. For Pune AI hiring outlook, see AI Classes in Pune for Freshers — Skills That Matter Most and Pune IT Salary Guide 2026.