Technical Ai Governance Manager

1 week ago

Yelahanka, India Aurigo Software Technologies Full time

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. - 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 - 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

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