Senior AI Data Engineer/Architect
Title: Senior AI & Data Engineer / Architect Location: Remote Role overview You will own our AI agent's path from prototype to production: tuning retrieval, adding evaluation and guardrails, and challenging the AI team where a workaround will not hold up. You will also set Data Quality standards, build trusted / Golden Layer datasets for AI, and contribute to a multi-agent platform on Gemini and Azure. There is little room for experimentation; we need fast, practical delivery. Key responsibilities Assess the current AI agent and lead the plan to production. Build and tune RAG pipelines: chunking, embeddings, hybrid search and grounding. Automate LLM / agent evaluation and regression testing in CI/CD. Add guardrails, human-in-the-loop review and monitoring. Challenge the AI team's technical approach; favour robust solutions over workarounds. Turn SME business rules into testable requirements and evaluation sets. Set Data Quality standards, profile data for trends and root causes, and build Golden Layer datasets. Help architect the multi-agent Wizard and shape the Data + AI roadmap. Required skills A RAG system built and deployed from scratch. Automated LLM / agent evaluation (e.g. DeepEval, RAGAS, promptfoo) in a release process. Pro-code agent development in Python (e.g. LangGraph, Semantic Kernel, Google ADK), beyond low-code builders. Gemini, Azure OpenAI / AI Foundry or similar enterprise LLM platforms. Guardrails, hallucination controls and AI observability. Data Quality: profiling, validation, monitoring, Golden Record creation and insight from data. Strong Python and advanced SQL; cloud data pipelines (e.g. Databricks, BigQuery, Azure). Confidence to challenge engineers with evidence; strong stakeholder communication. Available in EST hours. Preferred experience SAP S/4HANA data: hands-on work; leading a full ERP implementation is a strong plus. Master Data Management (MDM). Supply chain, manufacturing or operational data. Medical Devices / Life Sciences, including regulated data handling. Enterprise data governance and data catalog tools. LLMOps / MLOps practices: prompt and model versioning, cost and latency monitoring.