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 ResponsibilitiesA. Data Governance (50%)Framework & StewardshipDesign and run enterprise Data Governance framework, policies, and RACI for data owners/stewardsEstablish Data Governance Council and operating model across Product, Engineering, Analytics, and BusinessDefine KPIs: catalog coverage, data quality score, policy adherenceData Quality, Catalog & LineageImplement business glossary, data catalog (Collibra / Alation / Purview / DataHub), and end-to-end lineageDefine and monitor data quality rules, SLAs, anomaly detection for critical domains (Customer, Product, Transaction)Manage data classification, PII/PHI tagging, retention, and access control policiesCompliance & SecurityEnsure compliance with PDPA, GDPR, CCPA and internal security standardsPartner with DPO / Legal / GRC for consent, purpose limitation, anonymization, and audit readinessOwn access governance - RBAC/ABAC for data warehouse, lakehouse, and feature storeB. MLOps & AI Governance (50%)ML Lifecycle & PlatformOwn MLOps best practices: from feature engineering -> training -> validation -> deployment -> monitoringBuild and manage ML platform components: Feature Store (Feast / Tecton / SageMaker Feature Store), Model Registry (MLflow / SageMaker Model Registry), Experiment TrackingStandardize CI/CD/CT for ML with Git, Docker, Airflow / Kubeflow / SageMaker PipelinesModel Governance & Responsible AIImplement Model Governance: model inventory, model cards, lineage (data -> features -> model -> endpoint), approval workflowsEnforce responsible AI checks: bias/fairness, explainability, drift, and reproducibilityAlign with AI Governance frameworks: NIST AI RMF, Singapore Model AI Governance Framework, AI Verify, ISO 42001Monitoring & OperationsImplement monitoring for data drift, concept drift, feature skew, and model performance degradationSet up alerting, automated retraining triggers, and rollback strategiesOptimize model serving costs, latency, and scalability on AWS / Azure / GCPTech Stack You Will Work WithGovernance: Collibra, Alation, Purview, Informatica, DataHub, AWS Glue, Apache AtlasData: Snowflake / BigQuery / Redshift, S3 / GCS, dbt, Airflow, Spark, KafkaMLOps: MLflow, Kubeflow, SageMaker, Vertex AI, Feast, Evidently, Great Expectations, Docker, Kubernetes, GitHub ActionsLanguages: Python (must), SQL (must), PySparkRequirements6-10 years total in Data Engineering / Data Governance / MLOpsAt least 2+ years owning data governance and at least 2+ years deploying ML models to productionStrong hands-on with DAMA-DMBOK and MLOps principlesProven experience setting up Model Registry, Feature Store, and monitoring for production ML systemsDeep understanding of PDPA/GDPR, data security, and AI riskExcellent stakeholder management - you can talk to both Data Scientists and Risk/LegalNice-to-HaveCDMP, AWS Certified ML Specialty, or similarExperience with LLM / GenAI governance - prompt logging, RAG governance, hallucination monitoringExperience with Great Expectations, Monte Carlo, Evidently AIIndustry experience in Media, FinTech, or other regulated industryWhat Success Looks Like in 12 MonthsTop 5 data domains governed with SLAs and quality monitoring >95%100% of production models registered with model cards, lineage, and approval workflowAutomated drift detection live for all critical models with Data catalog adoption >80% and zero compliance audit findings