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 2hr alert SLA

Data catalog adoption 80% and zero compliance audit findings