Ai/ Data DevOPS/ SRE

Newbridge; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

Newbridge is building trusted data and trusted AI to ensure client data is accurate, compliant, and governed, with ML models that are reproducible, monitored, and responsibly deployed to production. This role is 50% Data Governance, 50% MLOps / ML Platform Governance.

Your responsibilities include implementing enterprise governance, data quality, cataloging, lineage, and secure access, while also standardizing ML lifecycle practices, model registry, feature store and monitoring across cloud

Qualifications

• 6-10 years total in Data Engineering / Data Governance / MLOps.
• At least 2+ years owning data governance and 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.

Responsibilities

• Data Governance: design and run enterprise governance framework, policies, and KCIs.
• Establish governance council across product, engineering, analytics and business.
• Implement data quality rules, cataloging, lineage, and retention policies.
• Compliance: PDPA/GDPR/CCPA and security standards; RBAC/ABAC for access governance.
• MLOps: own CI/CD/CT for ML, feature store, model registry, experiment tracking; monitoring.

Skills

Data Governance

MLOps

Python

SQL

Stakeholder management

Tools

Snowflake

BigQuery

Airflow

Kubeflow

SageMaker

MLflow

Docker

Kubernetes

dbt

Spark

Job description

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
• 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

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