Applied AI engineer

End-to-end RAG pipelines: retrieval architecture, chunking strategy, re-ranking, and prompt design

Evaluation frameworks: defining quality metrics, building eval harnesses, and tracking pipeline health over time

Agentic workflows and LLM integration: multi-step reasoning, tool use, orchestration, model selection, context management, latency, and cost optimisation

Collaboration with Backend engineers to serve NLP outputs at production quality and latency

• Research-to-production translation: staying current and knowing what’s worth shipping