Hiring for GenAI Intern
We are looking for a GenAI Intern with strong experience in LLM-based systems, Retrieval-Augmented Generation (RAG) and structured data integration. The ideal candidate has hands-on experience building production-grade AI chatbots that combine LLMs, databases, vector stores and guardrails, especially in healthcare or sensitive data environments. As GenAI Intern, you will work on developing and fine-tuning advanced AI models, exploring GenAI, LLMs, Deep Learning applications. You will gain hands-on experience with state-of-the-art AI tools and frameworks while working alongside experienced AI professionals.
Location: Pune, India
Key Responsibilities :
- Design and build LLM-powered chatbots using structured (SQL/NoSQL) and unstructured data.
- Implement intent classification, guardrails and safe response generation
- Build hybrid AI pipelines combining:
- Databases (MySQL/PostgreSQL or equivalents)
- Vector databases (Qdrant, Pinecone, Weaviate, etc.)
- LLMs (OpenAI, LLaMA, Mistral, etc.)
- Ensure accurate, deterministic handling of factual queries (counts, dates, records)
- Implement RAG pipelines for summarization and semantic retrieval (not for factual counting)
- Design non-SQL abstractions (API layers, event indexes, knowledge graphs when needed)
- Debug hallucination, incorrect counts, and reasoning failures in LLM outputs
- Implement observability, logging, evaluation, and regression testing for GenAI systems
- Collaborate with backend, frontend, and product teams
- Explain technical decisions clearly to non-AI stakeholders
Required Technical SkillsCore GenAI & LLM
- Strong experience with LLMs (LLaMA, Mistral, Claude, etc.)
- Prompt engineering for:
- System prompts
- Intent classification
- Tool/function calling
- Safety & refusal handling
- Understanding of LLM limitations (hallucinations, counting errors, reasoning limits)
RAG & Embeddings
- Hands-on experience with RAG architectures
- Embedding models (e.g., BGE, E5, OpenAI embeddings)
- Vector databases:
- Qdrant (preferred)
- Pinecone / Weaviate / FAISS
- Ability to decide when NOT to use vector search
Backend & Data
- Strong backend experience with Python
- Experience with Flask / FastAPI
- Working knowledge of:
- SQL (MySQL/PostgreSQL) OR structured alternatives
- Data modeling for event-based systems
- Experience building API layers over data sources
System Design
- Intent-based routing (COUNT, HISTORY, SUMMARY, OUT_OF_SCOPE, etc.)
- Guardrails, policy enforcement and validation layers
- Caching, performance optimization and scalability.
Who Can Apply?
We are looking for candidates who:
✅ Are available for a 6-month internship.
✅ Have completed projects in GenAI, LLM, Langchain, AI and NLP
✅ Have a strong understanding of LLMs, Transformers, NLP
✅ Are proficient in Python, SQL
✅ Are passionate about AI and eager to learn and innovate.
Perks & Benefits:
- Stipend - INR 5K-10K
- Certificate & Letter of Recommendation upon completion.
- Flexible Work Hours – Work at your convenience.
- Exposure to Cutting-Edge AI Technologies.
- Collaborative Learning Environment with industry experts.
Nice-to-Have (Bonus Skills)
- Knowledge graphs (Neo4j, RDF, etc.)
- LLM evaluation frameworks
- Multi-agent systems
- CI/CD for AI systems, DevOps
- Model observability & monitoring
What We Value Most
- Ability to debug and explain failures
- Ownership mindset
- Clear thinking over hype
- Knowing when not to use LLMs
- Strong fundamentals in data & system design
Experience Level : you must have good experience for hands-on with GenAI / LLM systems
How to Apply? :
If you are interested in this exciting opportunity, send your resume to [email protected] with the subject line "Application for GenAI Internship – Ai India". with (Optional) GitHub / blog / architecture diagrams. Thank you!
Job Type: Internship
Pay: ₹5,000.00 - ₹15,000.00 per month
Application Question(s):
- Which of the following AI frameworks have you used?
TensorFlow
PyTorch
Hugging Face Transformers
LangChain
OpenCV
YOLO
- Do you have experience working with Generative AI models?
- Have you worked on projects involving Deep Learning and Computer Vision?
Work Location: In person
