AI Platform Engineer

Key Responsibilities

• Deliver projects within the assigned practice to meet platform availability, reliability and resilience requirements.
• Convert business requirements into platform infrastructure design.
• Partner with service providers to design, deploy and configure resilient, available and secure AI infrastructure.
• Ensure alignment with the Authority's Enterprise Architecture and Security Standards, including IM8 and AI Policies.
• Serve as the primary point-of-contact for new AI product initiatives, providing support for system design, integration, acceptance and performance testing.
• Oversee AI platform operations and respond to queries from Data Scientists and Product Managers.
• Enhance operational runbooks and SOPs while adhering to enterprise and compliance requirements.
• Drive continuous improvements to the AI platform user experience through new features and enhancements.
• Enable faster and simpler product development and operations by leveraging automation and building reusable services.
• Support AI platform design review and contribute to improvements in 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, including the ability to pitch ideas, influence stakeholders, and balance strategic thinking with business needs and challenges.
• Ability to evaluate project scope and compliance requirements and create 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 processing of structured and unstructured datasets is advantageous.

Good understanding of AI,application and infrastructure security risks and controls.