Technical AI governance Manager

Key Responsibilities:

Define and maintain technical standards for the full ML lifecycle, including data sourcing, quality, feature engineering, training, evaluation, deployment, monitoring, and retirement.

Establish governance checkpoints for model validation, approval, reproducibility, traceability, and

documentation.

Design and maintain scalable MLOps pipelines with CI/CD, automated testing, monitoring workflows, and robust versioning for models, data, and experiments.

Create guidelines for data lineage, feature stores, metadata management, and model repository practices.

Enforce requirements for model explainability, interpretability, robustness, and technical design reviews.

Build monitoring frameworks covering model performance, data drift, concept drift, fairness, anomalies, and related alerts.

Coordinate model retraining, calibration, updates, and sunset workflows based on monitoring and lifecycle needs.

Produce and maintain comprehensive technical documentation (model cards, datasheets, evaluation reports, architecture diagrams, lifecycle logs).

Ensure audit readiness and support internal governance, regulatory, and customer audit requirements while mitigating technical risks and AI vulnerabilities.

Collaborate cross-functionally and provide technical leadership, guidance, and mentorship to engineering, data science, product, and governance teams.

Required Skills & Qualifications:

Technical Skills

Strong understanding of ML/AI concepts, model architectures, training methodologies, and data workflows.

Hands-on experience with MLOps tools such as MLflow, Kubeflow, Azure ML, Vertex AI, or AWS SageMaker.

Solid grasp of data engineering principles, feature stores, and metadata management.

Experience with model monitoring, drift detection, and AI observability tools.

Strong software engineering fundamentals (Python, APIs, DevOps, CI/CD).

Governance & Quality Skills

Experience creating engineering standards, reviewing technical designs, or managing lifecycle governance.

Practical understanding of responsible AI topics (bias, fairness, explainability, robustness).

Familiarity with governance frameworks (NIST AI RMF, ISO/IEC 42001) is a plus.

Education & Experience

Bachelor's or master's degree in computer science, AI/ML, Data Science, or a related field.

510+ years of experience in ML Engineering, MLOps, Data Engineering, or related roles.

Experience in enterprise SaaS or high-assurance sectors is advantageous.

Soft Skills

Excellent technical documentation and communication skills.

Ability to lead cross-functional technical discussions and influence engineering decisions.

Strong analytical and problem-solving mindset.

Preferred Certifications

AWS Machine Learning Specialty

Microsoft Responsible AI Certification (RAI Engineer)

Google Responsible AI Professional Certificate