Job Description
Experience: 3-8 Years
Location: Mumbai
RESPONSIBILITIES
- Implement RAG pipelines — document ingestion, chunking, embedding, vector store loading, retrieval and response generation
- Build agentic workflows — LLM-driven processes with tool use, loops and error handling
- Integrate LLM APIs into product features — extract, classify, summarise and generate structured outputs
- Write and maintain an LLM evaluation suite — test cases, metrics, regression tracking
- Build FastAPI endpoints that expose AI capabilities to other services and front-ends
- Participate in design reviews and contribute to architecture decisions
REQUIRED SKILLS
- Python — clean, reviewable, testable code. Not script-level, production-level.
- LLM API usage — OpenAI, Anthropic or Azure OpenAI SDK. Streaming, function calling, token management.
- RAG implementation — has built at least one retrieval pipeline end-to-end
- Vector databases — has loaded and queried at least one (Pinecone, Chroma, Weaviate, pgvector)
- FastAPI — has built and deployed at least one REST API
- Git and Docker — standard development workflow
ADDITIONAL SKILLS (increase placement level)
- LangChain, LlamaIndex or LangGraph X— used in a real project, not documentation examples
- LLM evaluation — has built automated test suites, not just manual review
- Agent design — can design a tool-use loop with memory and error recovery, not just implement one Fine-tuning — has fine-tuned a model (LoRA/QLoRA) and understands when to use it
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