Key Responsibilities
Design, train, evaluate, and ship machine learning and deep learning models (classification, regression, ranking, vision, NLP, time series) against well-defined business and scientific problems.
Design and implement LLM-powered applications using major model providers (Claude/Anthropic, Azure OpenAI, OpenAI, or equivalents).
Build retrieval-augmented generation (RAG) systems, including chunking strategies, embeddings, vector store selection (e.g., Azure AI Search, pgvector), and re-ranking.
Develop agentic workflows with tool use and orchestration; implement guardrails, evaluation harnesses, and human-in-the-loop review where appropriate.
Apply prompt engineering, fine-tuning, and structured output techniques; measure quality with offline evals and online metrics.