AI Lead

Job description

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