AI Solutions Engineer
Summary
Develops and operationalises production AI/ML solutions for Singapore's energy regulator: designing and deploying models across the full lifecycle, running MLOps (CI/CD/CT pipelines, automated retraining, drift detection, model governance), and translating AI insights for stakeholders. Core stack: Python, PyTorch, TensorFlow, Pandas.
About the role
The role is responsible for developing and operationalising AI solutions to support critical energy sector operations and regulatory functions. The incumbent will collaborate with data scientists and engineers to design, test, and deploy AI/ML models across the full lifecycle, ensuring solutions are production-ready and scalable. The role also establishes and manages robust MLOps practices, including CI/CD/CT pipelines, automated retraining, performance monitoring, and model governance to maintain reliability and safety. In addition, the incumbent provides technical leadership and stakeholder engagement, translating complex AI insights into actionable outcomes while aligning business objectives, technical feasibility, and compliance requirements.
Key responsibilities
Work collaboratively with Data Scientists and Data Engineers to design, test, and implement AI/ML models for 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
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
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
About you
Higher tertiary background in Computer Science, Mathematics, Physics, Data Science or related quantitative field
Specialised AI certifications advantageous
5-10 years of professional experience developing, deploying, and maintaining enterprise-grade AI/ML models in production environments
Demonstrable experience across the full MLOps lifecycle
Expert proficiency in AI/ML modelling including supervised/unsupervised learning, deep learning, and reinforcement learning
Advanced programming skills in Python and frameworks such as PyTorch, TensorFlow, Pandas, and equivalent technologies
Comprehensive knowledge of MLOps tools and practices for automated model training, testing, deployment, and monitoring
Experience with model optimisation techniques including quantisation, pruning, and knowledge distillation
Exceptional stakeholder management abilities with proven track record of translating technical concepts for diverse audiences
Systems thinking approach to problem-solving within complex technical constraints