AI Governance & MLOps Engineer

Responsibilities

• Design and maintain ML model registries, versioning, and lifecycle workflows.
• Build real-time model monitoring for drift, bias, performance degradation, and data-quality regressions.
• Implement AI governance frameworks aligned with GDPR, the EU AI Act, NIST AI RMF, and US sectoral regulators (HIPAA, NYDFS, FFIEC).
• Create audit trails and explainability layers for enterprise AI systems — including LIME/SHAP for tabular models and structured logging for LLM apps.
• Partner with AI engineers and customer compliance teams to operationalize responsible AI.

Requirements

• 5+ years in ML engineering or data science with MLOps depth.
• Hands-on experience with MLflow, Weights & Biases, or Azure ML.
• Working knowledge of AI fairness, robustness, and explainability techniques.
• Strong Python; experience with monitoring frameworks (Evidently, Fiddler, WhyLabs, or similar).
  • Comfort working at the intersection of engineering, risk, and audit.