AI Lead

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