AI Engineer 2 (Data & AI Department)
AI Engineer at Singapore's Energy Market Authority designing, building, and deploying production-ready AI/ML models for energy-sector operations and regulatory functions. Day-to-day centers on the full ML lifecycle with heavy MLOps work (CI/CD/CT pipelines, versioning, monitoring, drift detection) plus stakeholder management and technical leadership.
- Compensation
- Not disclosed
- Location
- Singapore, Singapore
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Design, test, and implement AI/ML models across the complete AI/ML lifecycle
- Develop production-ready AI solutions deployable reliably for critical energy sector operations
- Establish and maintain MLOps foundations from code commit to model retirement
- Design CI/CD/CT pipelines and manage version control across code, data, and models
- Implement automated retraining, performance monitoring, data drift detection, and guardrail systems
- Lead engagement with senior management, data scientists, business users, and vendor/IT teams
- Translate complex AI outputs into insights for non-technical audiences
Full description
(1) AI Solution Development: Work collaboratively with Data Scientists and Data Engineers to design, test, and implement AI/ML models for EMA's diverse use cases across the complete AI/ML lifecycle. Lead the development of production-ready AI solutions that can be deployed reliably, timely, and consistently to support critical energy sector operations and regulatory functions.
(2) MLOps and Production Lifecycle Management: Establish and maintain comprehensive MLOps foundations covering the end-to-end journey from code commit to model retirement. Design continuous integration, continuous deployment, and continuous training (CI/CD/CT) pipelines whilst managing version control across code, data, and models. Implement automated retraining infrastructure with performance monitoring, data drift detection, and guardrail systems to ensure models remain safe and effective in critical infrastructure environments.
(3) Stakeholder Management and Technical Leadership: Lead engagement and alignment initiatives with senior management, data scientists, business users, and vendor/IT teams. Translate complex AI outputs into actionable insights and business intelligence for non-technical audiences. Bridge communications between diverse stakeholders to identify optimal trade-offs between business outcomes, system performance limitations, and compliance requirements.