Machine Learning Engineer

Role Overview

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%)

1. Framework & Stewardship
2. • 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
3. Data Quality Catalog & Lineage
4. • 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
5. Compliance & Security
6. • 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%)

1. ML Lifecycle & Platform
2. • 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
3. Model Governance & Responsible AI
4. • 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
5. Monitoring & Operations
6. • 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 <2hr alert SLA
• Data catalog adoption >80% and zero compliance audit findings