ML Ops

Newbridge; Singapore; Sur place; SGD 150 000 - 190 000; Plein temps

Résumé du poste

Newbridge seeks a leader to build trusted data and trusted AI, blending Data Governance with MLOps to ensure data accuracy, security, and responsible model deployment.

The role covers governance framework, cataloging, lineage, compliance with PDPA/GDPR, and monitoring across enterprise ML systems, partnering with product, engineering, and risk teams.

Qualifications

• 6-10 years total in Data Engineering / Data Governance / MLOps.

Responsabilités

• Design and run enterprise Data Governance framework, policies, and RACI for data owners/stewards.
• Define KPIs: catalog coverage, data quality score, policy adherence.
• Implement business glossary, data catalog with end-to-end lineage, and data quality rules.
• Own MLOps best practices: feature engineering to deployment and monitoring.
• Implement model governance: model cards, lineage, and approval workflows.
• Set up monitoring for data drift, concept drift, and model performance.

Connaissances

Python

SQL

PySpark

Data Governance

MLOps

Data Engineering

Stakeholder management

Outils

Collibra

Alation

Purview

Informatica

DataHub

AWS Glue

Apache Atlas

Snowflake

BigQuery

Redshift

S3

GCS

dbt

Airflow

Spark

Kafka

MLflow

Kubeflow

SageMaker

Vertex AI

Feast

Evidently AI

Great Expectations

Docker

Kubernetes

GitHub Actions

Description du poste

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