Senior AI Engineer

Deep GenAI & RAG Expertise: 2+ years of hands-on experience building and productionizing LLM applications, with deep technical knowledge of vector databases (e.g., Qdrant, Weavite, Pinecone, pgvector) and orchestration layers (e.g., LangGraph, LlamaIndex). Comfortable working with heterogeneous documents like pdfs. Experience working with re-ranking models.

Production-Grade Asynchronous Python: Strong software engineering fundamentals with deep experience in asynchronous programming (asyncio, FastAPI) to handle highly concurrent, I/O-bound LLM and database calls.

Advanced Context Engineering: Proven experience solving production RAG challenges such as context window management, metadata filtering and semantic routing. Experience leveraging coding harnesses like Claude code.

Modern Software Practices: Mastery of Git, automated testing and CI/CD pipelines. Experience working with cloud platforms like AWS.

Comfort with Ambiguity: Ability to rapidly prototype an approach, validate it with metrics, and pivot fast in a fast-moving environment.

It would be great if you had

GraphRAG Experience: Familiarity with Knowledge Graphs (e.g., Neo4j) and ontologies to enhance standard vector RAG with highly structured, interconnected corporate data.

Local/Open-Weight Model Deployment: Experience deploying and fine-tuning open-weight models (like Llama 3 or Mistral) via frameworks like vLLM or Ollama to optimize token costs and privacy.

Prompt Caching & Cost Optimisation: Practical experience implementing semantic caching layers to drastically reduce LLM API billing and response latency.