Senior AI Engineer

Build production-grade AI systems with focus on retrieval quality, agent orchestration, and reliability at scale
Take ownership of the full RAG pipeline from document ingestion and chunking strategy through retrieval design, evaluation, model integration, and stateful agent workflows
Design chunking and ingestion architecture with deterministic, validation-gated pipelines handling diverse enterprise document types with indexing and contextual enrichment
Develop retrieval pipeline including vector search, BM25 hybrid retrieval, RRF re-ranking, embedding model selection and optimisation
Design stateful, multi-step agent workflows with LangGraph; build chains and tool-use patterns with LangChain
Implement observability including tracing, debugging, and monitoring pipeline performance end-to-end with LangSmith
Evaluate and tune models using RAGAS-based evaluation frameworks, benchmarking retrieval recall and answer faithfulness
Deploy open-weight LLM models (Llama 4, Qwen3, MiMo-V2) under memory and latency budgets via Amazon Bedrock and self-hosted inference
Design async workflow orchestration pipelines with Temporal.io; containerised deployment on Kubernetes via GitLab CI