AI Engineer 2 (Data & AI Department)

Energy Market Authority (EMA); Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Energy Market Authority (EMA) in Singapore seeks an AI/ML specialist to design, test, and implement AI/ML models for EMA's regulatory use cases, collaborating with data scientists and engineers to deliver production-ready solutions.

Role emphasizes MLOps and end-to-end lifecycle management, CI/CD/CT pipelines, automated retraining, monitoring, and governance, plus stakeholder leadership to translate AI outputs into actionable business insights for non-technical audiences.

Qualifications

• Experience in AI/ML model lifecycle design and deployment.
• Knowledge of CI/CD/CT pipelines and MLOps best practices.
• Strong stakeholder management and ability to translate AI outputs to business insights.

Responsibilities

• Design, test, and implement AI/ML models for EMA use cases.
• Develop production-ready AI solutions with reliable deployment.
• Lead end-to-end MLOps lifecycle management and governance.

Job 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.