Machine Learning Engineer (Ops)

You will build trusted data and trusted AI - ensuring our clients data is accurate, compliant, and governed, and our ML models are reproducible, monitored, and responsibly deployed to production. This role is 50% Data Governance, 50% MLOps / ML Platform Governance. Key Responsibilities A. Data Governance (50%) • Framework & Stewardship • • Design and run enterprise Data Governance framework, policies, and RACI for data owners/stewards • Establish Data Governance Council and operating model across Product, Engineering, Analytics, and Business • Define KPIs: catalog coverage, data quality score, policy adherence • Data Quality, Catalog & Lineage • • Implement business glossary, data catalog (Collibra / Alation / Purview / DataHub), and end-to-end lineage • Define and monitor data quality rules, SLAs, anomaly detection for critical domains (Customer, Product, Transaction) • Manage data classification, PII/PHI tagging, retention, and access control policies • Compliance & Security • • Ensure compliance with PDPA, GDPR, CCPA and internal security standards • Partner with DPO / Legal / GRC for consent, purpose limitation, anonymization, and audit readiness • Own access governance - RBAC/ABAC for data warehouse, lakehouse, and feature store • B. MLOps & AI Governance (50%) • ML Lifecycle & Platform • • Own MLOps best practices: from feature engineering -> training -> validation -> deployment -> monitoring • Build and manage ML platform components: Feature Store (Feast / Tecton / SageMaker Feature Store), Model Registry (MLflow / SageMaker Model Registry), Experiment Tracking • Standardize CI/CD/CT for ML with Git, Docker, Airflow / Kubeflow / SageMaker Pipelines • Model Governance & Responsible AI • • Implement Model Governance: model inventory, model cards, lineage (data -> features -> model -> endpoint), approval workflows • Enforce responsible AI checks: bias/fairness, explainability, drift, and reproducibility • Align with AI Governance frameworks: NIST AI RMF, Singapore Model AI Governance Framework, AI Verify, ISO 42001 • Monitoring & Operations • • Implement monitoring for data drift, concept drift, feature skew, and model performance degradation • Set up alerting, automated retraining triggers, and rollback strategies • Optimize model serving costs, latency, and scalability on AWS / Azure / GCP Tech Stack You Will Work With Governance: Collibra, Alation, Purview, Informatica, DataHub, AWS Glue, Apache Atlas Data: Snowflake / BigQuery / Redshift, S3 / GCS, dbt, Airflow, Spark, Kafka MLOps: MLflow, Kubeflow, SageMaker, Vertex AI, Feast, Evidently, Great Expectations, Docker, Kubernetes, GitHub Actions Languages: Python (must), SQL (must), PySpark Requirements • 6-10 years total in Data Engineering / Data Governance / MLOps • At least 2+ years owning data governance and at least 2+ years deploying ML models to production • Strong hands-on with DAMA-DMBOK and MLOps principles • Proven experience setting up Model Registry, Feature Store, and monitoring for production ML systems • Deep understanding of PDPA/GDPR, data security, and AI risk • Excellent stakeholder management - you can talk to both Data Scientists and Risk/Legal Nice-to-Have • CDMP, AWS Certified ML Specialty, or similar • Experience with LLM / GenAI governance - prompt logging, RAG governance, hallucination monitoring • Experience with Great Expectations, Monte Carlo, Evidently AI • Industry experience in Media, FinTech, or other regulated industry What Success Looks Like in 12 Months • Top 5 data domains governed with SLAs and quality monitoring >95% • 100% of production models registered with model cards, lineage, and approval workflow • Automated drift detection live for all critical models with 80% and zero compliance audit findings