Platform Engineer

Job Summary

As an Platform Engineer, you will manage multiple priorities in a fast-paced environment while supporting stakeholder needs and responding to change. Specifically, you are expected to:

• Partner with DAE Data Science and Product Management teams to productionise and host Data Science and AI products on PE-managed on-premises and cloud AI platforms.
• Work with Technology Group divisions—including Platform Architecture and Engineering, Cybersecurity, and Data & Collaboration Platforms—to define requirements and integrate scalable, secure, and compliant AI platforms into MAS’s enterprise environment.

Key Responsibilities

• Deliver projects within the assigned practice to meet platform availability, reliability and resilience requirements.
• Translate business requirements into platform infrastructure designs.
• Work with service providers to design, deploy and configure resilient, available and secure AI infrastructure.
• Ensure compliance with the Authority’s Enterprise Architecture and Security Standards, as well as IM8 and AI Policies.
• Act as the point-of-contact for new AI product initiatives, supporting system design, integration, acceptance and performance testing.
• Manage AI platform operations and address queries from Data Scientists and Product Managers.
• Improve operational runbooks, SOPs and adhere to enterprise and compliance mandates.
• Continually improve AI platform experience by implementing new features and enhancements.
• Continually simplify product development and operations through automation and building reusable services.
• Assist in AI Platform design review and improvement of AI, OS, container and database security standards.
• Onboard hosting environments by designing, setting up, configuring, implementing, testing and commissioning the required infrastructure.
• Manage system architecture, resource planning, platform performance, and hosting across servers, operating systems, and containers, excluding hypervisor and storage layers.

What we are looking for

• Strong written and verbal communication skills, with the ability to pitch ideas, influence stakeholders and balance strategic perspective with business needs and challenges.
• Ability to assess project and compliance requirements and develop practical, sustainable solutions.
• Hands-on experience building, securing and operating enterprise Data Science and AI platforms on AWS or in on-premises environments.
• Hands-on experience building and operating DevSecOps, MLOps, and/or LLMOps pipelines.
• Hands-on experience with AI and container services, LLM inference and Linux preferred.
• Hands-on experience with Infrastructure-as-Code, automation and Python tech stack preferred.
• Experience working with Identity & Access Management services (e.g. Entra ID, AWS Cognito and AWS IAM), and Monitoring & Observability services (e.g. CloudWatch, Splunk and Elastic) are advantageous.
• Knowledge of PostgreSQL, vector databases, object storage, and the processing of structured and unstructured datasets is advantageous.
• Good understanding of AI, application and infrastructure security risks and controls.