Senior AI-ML Platform Engineer / Tech Lead

Your Role

As a Senior AI/ML Platform Engineer and Technical Lead, you will be at the heart of building and evolving the AI/ML platform that powers intelligent capabilities across the organisation. In this role, you will lead a team of professional engineers — setting clear objectives, supporting their development, and being accountable for the quality, accuracy, and timeliness of the team's output. You will solve complex, operationally important engineering challenges by filtering and prioritising inputs from a wide range of internal and external sources, drawing on an in-depth understanding of how platform sub-functions work together. Acting as a key technical liaison across Cloud, Cybersecurity, Governance, and adjacent engineering teams, you will ensure the platform remains aligned with enterprise standards and emerging best practices. Your decisions will be guided by resource availability and organisational objectives, and you will contribute to defining team responsibilities, engineering standards, and platform governance practices that raise the quality bar across closely related teams.

Who You Are

• 8 or more years of relevant industry experience in software or platform engineering, including at least 2 years working with large language model operations (LLMOps) in a production environment.

• Hands-on experience with LLM/API gateway technologies such as LiteLLM or comparable solutions, and with multi-provider integration across major cloud AI platforms including Azure OpenAI, Azure AI Foundry, and AWS Bedrock.

• Practical experience with cloud-native technologies on AWS and/or Azure, including container orchestration (such as ECS Fargate), secrets management, relational and non-relational databases, networking, and load balancing.

• Proficiency in developing production-grade backend services using Python and asynchronous programming frameworks; experience with Rust is a plus.

• Experience with infrastructure-as-code and CI/CD tooling, including containerisation with Docker, pipeline automation with Azure DevOps, and cloud provisioning frameworks such as Terraform or CloudFormation.