About the role
This is your chance to work on cutting-edge GenAI, LLM fine-tuning, and agent frameworks and see your code
power products used in the real world. If you’re excited about experimenting, shipping fast, and solving complex AI
challenges hands-on, you’ll love it here.
Real production exposure (not dummy projects)
Hands-on learning in GenAI, LLMs, RAG, and multi-agent systems
Mentorship from experienced AI engineers
High-conversion opportunity to full-time role
What you'll own
- Build working AI agents using LangChain / LangGraph
- Develop RAG pipelines using vector databases (FAISS, Pinecone, ChromaDB)
- Create FastAPI endpoints to expose AI workflows
- Experiment with prompt engineering and retrieval strategies
- Implement tool integrations (APIs, basic MCP concepts)
- Contribute to internal AI prototypes and innovation initiatives
- Clearly document architecture, trade-offs, and learnings
What we're looking for
- We are looking for highly driven AI/ML Interns who want hands-on exposure to building agent-based AI systems,
- RAG pipelines, and production-grade GenAI applications. This is a builder-focused role not research-only.
- Strong foundation in Python and ML fundamentals
- Built at least one working RAG or agent-based system independently
- Familiar with LangChain/LangGraph or similar frameworks
- Deployed at least one project (GitHub + live demo preferred)
- Able to explain design decisions confidently
- Strong curiosity and ownership mindset.
- Strong CS fundamentals (DSA + problem-solving) are a baseline expectation - not optional.