Aurigo - Technical AI Governance Manager

Description :

- 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 :

- 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 :

- 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 :

- 5- 10+ years of experience in ML Engineering, MLOps, Data Engineering, or related roles.

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

Soft Skills :

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

- Strong analytical and problem-solving mindset.

Preferred Certifications :

- Microsoft Responsible AI Certification (RAI Engineer)

- Google Responsible AI Professional Certificate