Senior AI & Data Quality Engineer / Architect

This is a W2 position. Need only USC/GC.

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

Hands-on production delivery is required; conceptual or proof-of-concept experience alone is not enough.

• 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.