AI Governance Engineer

Quick Facts

• Role: AI Governance Engineer

• Focus: Technical AI governance for ML/LLM lifecycle—controls, testing, monitoring, and regulatory-aligned documentation

Description

Implement technical mechanisms for AI governance across the ML model lifecycle, including model approval, versioning, model registry, data lineage, access control, and audit logging. Design and automate testing/evaluation for AI models and LLM applications (bias & fairness, performance, robustness, red teaming, guardrails, content safety) and automate generation of audit documentation (Model Cards, evaluation reports, Data Sheets) within ML pipelines. Set up production monitoring (drift and performance), incident detection and alerting, and implement controls aligned with EU AI Act, ISO/IEC 42001, and NIST AI RMF while integrating governance inventory with ML platforms and data catalogs.

Responsibilities

• Implement AI Governance controls in the ML lifecycle (Model Registry, approvals, versioning, lineage, access control, audit logging)

• Build and automate evaluation tests for AI/LLM applications (bias & fairness, performance, robustness, red teaming, guardrails, content safety)

• Automate technical/audit documentation in ML pipelines (Model Cards, evaluation reports, Data Sheets)

• Implement model monitoring in production (drift detection, performance monitoring, incident detection and alerting)

• Implement EU AI Act / ISO/IEC 42001 / NIST AI RMF-aligned technical controls with AI governance consultants

• Integrate AI Inventory and AI Governance tools with ML platforms, data catalogs, and Databricks environments

• Create reusable test suites, evaluation templates, and Controls-as-Code solutions

Requirements

• Minimum 5 years of experience as a Machine Learning Engineer, MLOps Engineer, or Data Scientist, including production experience

• Very strong Python and automation of ML processes

• Practical experience with MLflow or similar model lifecycle management, plus at least one cloud ML platform (Databricks, Azure ML, AWS SageMaker, or Vertex AI)

• Experience in testing and evaluating AI models and LLM applications; knowledge of tools such as Fairlearn, AIF360, Evidently, Giskard, LangSmith, or Azure AI Content Safety

• Knowledge of model monitoring, bias & fairness, red teaming, guardrails, and AI risk management

• Ability to collaborate with technical and business teams and present results to non-technical stakeholders

• Fluent English

Benefits

• Stable B2B collaboration on an international strategic project (initial 6-month contract with a high likelihood of long-term extension)

• Unique opportunity at the intersection of advanced engineering (MLOps / LLMs) and cutting-edge global tech regulations

• Access to modern enterprise cloud stacks (Databricks, Azure, AWS)

• Work in a highly qualified, multidisciplinary team