Production RAG & LLM Engineering

What you will build

• Design a RAG architecture that handles large, changing, permission-scoped data
• Ingest PDFs and text with chunking, metadata and selective vectorisation
• Implement BM25, vector and hybrid retrieval on PostgreSQL + pgvector
• Generate grounded, cited answers with context budgeting across LLM providers
• Orchestrate agentic query flows with LangGraph, tools and guardrails
• Run async pipelines with Kafka and Redis workers and track job state
• Add multi-tenant auth, evaluation, Prometheus/Grafana monitoring and CI
• Ship with Docker and operate it in production