Platforms Engineer, MLOps

AI Singapore (AISG) is Singapore's national programme in artificial intelligence, launched by the National Research Foundation (NRF) to anchor deep national capabilities in AI. Hosted at Nanyang Technological University (NTU), AI Singapore brings together Singapore-based research institutions and the vibrant ecosystem of AI start-ups and companies to perform use-inspired research, grow knowledge, create tools, and develop the talent to power Singapore's AI efforts. Since our inception in 2017, we have established a culture of respect, continuous learning, experimentation and curiosity, centred around innovation.

The Platforms Engineering Group builds and operates the infrastructure and systems that enable AI practitioners across AISG's programmes to develop, train, and deploy machine learning models at scale. The group comprises four teams: Infrastructure Operations (InfraOps), Data Operations (DataOps), Machine Learning Operations (MLOps), and Experiences (EX).

As a Platforms Engineer, MLOps in the Platforms Engineering Group, you will help build and operate modern infrastructure and systems to run large-scale machine learning and deep learning workloads. You will also design, develop, and maintain the AISG platform and tooling stack to enable project teams and partners to build products better and faster. You will serve as a domain expert to nurture AI talents and play a key part in driving the growth of Singapore's AI sector.

• Design, build and maintain the platform and tooling stack that empowers AISG engineers to deliver their work effectively.

• Serve as the liaison between Platforms Engineering and AI project teams, ensuring they have the right ML tooling, infrastructure, and CI/CD processes in place.

• Develop and implement MLOps practices and processes, including CI/CD pipelines for ML models, automated testing, and model monitoring and versioning.

• Collaborate with data scientists, data engineers, and other stakeholders to ensure that ML solutions meet AISG's needs and are aligned with operational goals.

• Build and maintain production infrastructure to be resilient, secure, and high-performing.

• Implement infrastructure-as-code to automate systems configuration, provisioning, deployment, and monitoring.

• Document and investigate issues arising from our systems when they occur.

• Develop tools and software that improve and automate infrastructure.

• Propose and drive technical decisions to completion.

• Mentor AISG Apprentices and contribute to the MLOps curriculum for the AI Apprenticeship Programme (AIAP).

To succeed in this role, you should have:

• At least 3 years of experience in an engineering or infrastructure position.

• Proficiency in at least one programming language such as Python, Go, Rust, or JavaScript/TypeScript (proficiency in Python and TypeScript is highly preferable).

• Proficiency in administering Linux systems.

• Proficiency in at least one automation tool (e.g., Ansible, Terraform, Bash).

• Familiarity with container and container orchestration technologies (e.g., Docker, Kubernetes, Helm).

• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.

• A systematic approach to development and engineering, including debugging, DevOps/MLOps practices, and agile development.

• Excellent communication skills, including the ability to express complex ideas clearly.

• Basic proficiency in machine learning concepts, including data analysis, predictive modelling, and model evaluation.

• Experience with ML experiment tracking tools (e.g., MLflow, Weights & Biases).

• Experience with infrastructure-as-code and GitOps workflows.

• Familiarity with observability tools (e.g., Prometheus, Grafana).

• Experience mentoring or training others in a technical capacity.

Candidates with relevant professional experience or graduates of the AI Singapore AI Apprenticeship Programme (AIAP) are welcome to apply.

We offer a dynamic environment at the forefront of Singapore's national AI efforts, opportunities to work with cutting-edge AI/ML infrastructure, and a culture that values continuous learning and professional development.

We regret that only shortlisted candidates will be notified.