Platforms Engineer, MLOps

About the role

The Platforms Engineering Group at NTU builds and operates the infrastructure and systems that let AI practitioners develop, train and deploy machine learning models at scale. As a Platforms Engineer, MLOps, you will help build and run the infrastructure for large-scale ML and deep learning workloads, design and maintain the AISG platform and tooling stack, and act as a domain expert supporting AI talent and the growth of Singapore's AI sector.

Key responsibilities

• Design, build and maintain the platform and tooling stack used by AISG engineers.
• Act as liaison between Platforms Engineering and AI project teams, ensuring they have the right ML tooling, infrastructure and CI/CD processes.
• Develop and implement MLOps practices, including CI/CD pipelines for ML models, automated testing, and model monitoring and versioning.
• Work with data scientists, data engineers and other stakeholders so ML solutions meet AISG's needs.
• Build and maintain resilient, secure, high-performing production infrastructure.
• Use infrastructure-as-code to automate configuration, provisioning, deployment and monitoring.
• Document and investigate system issues, and develop tools 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.

Required profile

• At least 3 years of experience in an engineering or infrastructure position.
• Systematic approach to development and engineering, including debugging, DevOps/MLOps practices and agile development.
• Excellent communication skills.
• Candidates with relevant professional experience or graduates of the AI Singapore AI Apprenticeship Programme are welcome to apply.

Required skills

• Proficiency in at least one programming language such as Python, Go, Rust or JavaScript/TypeScript (Python and TypeScript highly preferable).
• Proficiency administering Linux systems.
• Proficiency in at least one automation tool such as Ansible, Terraform or Bash.
• Familiarity with container and orchestration technologies such as Docker, Kubernetes and Helm.
• Familiarity with cloud platforms such as AWS, Azure or Google Cloud Platform.
• Good-to-have: basic ML concepts, ML experiment tracking tools (MLflow, Weights & Biases), GitOps, observability tools (Prometheus, Grafana), and mentoring experience.