AI Governance & Explainability Engineer

Key Responsibilities

• AI Governance Engineering: Embed governance, explainability, and risk controls into AI/GenAI/Agentic AI workflows; translate policies into technical guardrails and automated CI/CD release gates.
• Explainability & Oversight: Define explainability outputs, implement human-in-the-loop controls, and document assumptions and residual risks.
• Data Governance Management: Operationalize AI governance in Microsoft Purview, including model registration, end-to-end lineage (Data → Models → Outputs), and metadata management.
• GenAI & Agentic AI Enablement: Apply governance to LLMs, RAG, and Agentic AI (observability, planning oversight, and retrieval transparency).
• Model Risk Controls: Implement feature attribution techniques (e.g., SHAP), identify bias signals, and generate audit-ready evidence.
• Monitoring & Incident Readiness: Define metrics for performance degradation, bias, and drift; support AI incident response.
• Release Gating: Enforce governance checkpoints within CI/CD pipelines (Dev → Test → Prod) to ensure documentation and artifact completeness.

Qualifications

• Education: Bachelor's or Master's degree in Computer Science, Information Systems, Data Science, Engineering, or related field.
• Experience: Minimum 7 years in AI/ML engineering, Data Science, GenAI/LLMs, NLP, or Data Governance.
• Technical Skills:
• Strong proficiency in Python.
• Experience with Microsoft Fabric (Lakehouse, OneLake, notebooks, pipelines).
• Experience with Microsoft Purview (catalog, lineage, classification).
• Familiarity with Azure AI Foundry / Azure ML, ML explainability libraries (e.g., SHAP), and LLMs/RAG architecture.
• Experience integrating governance into CI/CD pipelines (GitHub or Azure DevOps).

• Soft Skills: Analytical problem-solving, communication skills, ability to influence cross-functional teams, and strong organizational skills.

Preferred

• Experience in auto insurance or claims industry.
• Experience evaluating model training approaches for generative models.
• Familiarity with synthetic data governance.
• Experience with platforms like Databricks AI, Snowflake Cortex, or Dataiku.
• Experience in regulated industries (insurance, financial services, healthcare).

Work Environment

Full remote position requiring reliable high-speed internet access and a dedicated workspace.

Job Purpose Summary

Hands-on technical role responsible for ensuring AI, GenAI, and Agentic AI solutions are explainable, governable, auditable, and production-ready by embedding governance directly into the technology stack within the Microsoft Fabric and Purview ecosystem.

Job Details

• Location: Remote, Tampa
• Job Type: remote
• Posted: May 14, 2026
• Categories: Software Engineering

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