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Nanyang Technological University

Platforms Engineer - MLOps

Platform EngineeringUpdated on Nov 14
Full-timeSingaporeNo salary listed

About the Job

Requirements

  • 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, such as Ansible, Terraform, or Bash.
  • Familiarity with container and container orchestration technologies, such as Docker, Kubernetes, or 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.
  • Ability to express complex ideas clearly.

Responsibilities

  • Design, build, and maintain the platform and tooling stack that enables AISG engineers to deliver their work effectively.
  • Serve as the liaison between Platforms Engineering and AI project teams, ensuring teams have appropriate machine-learning tooling, infrastructure, and CI/CD processes.
  • Develop and implement MLOps practices and processes, including CI/CD pipelines for machine-learning models, automated testing, model monitoring, and versioning.
  • Collaborate with data scientists, data engineers, and other stakeholders to ensure machine-learning solutions meet AISG's needs and align with operational goals.
  • Build and maintain production infrastructure that is resilient, secure, and high-performing.
  • Implement infrastructure-as-code to automate systems configuration, provisioning, deployment, and monitoring.
  • Document and investigate issues arising from systems.
  • 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.

Desired Qualifications

  • Basic proficiency in machine-learning concepts, including data analysis, predictive modelling, and model evaluation.
  • Experience with machine-learning experiment tracking tools such as MLflow or Weights & Biases.
  • Experience with infrastructure-as-code and GitOps workflows.
  • Familiarity with observability tools such as Prometheus or Grafana.
  • Experience mentoring or training others in a technical capacity.

About the Company

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