Senior AI Engineering Manager

Overview

Ai Centre Of Excellence (Coe)

Senior Ai Engineering Manager

Senior Ai Engineering Manager

• , you will lead the development of the organisation’s
• GenAI and MLOps platform
• , ensuring that AI solutions can be built, deployed, and operated at scale.
• Working as part of a rapidly expanding AI CoE, you will oversee a team of engineers and contractors responsible for building the foundational platforms that support AI product development across multiple business lines.
• This role requires a leader who is
• highly technical and hands-on
• , capable of guiding architecture decisions while also contributing directly to engineering and platform development.
• Discover the Responsibilities:
• Lead the design and development of the organisation’s
• Generative AI and MLOps platform
• , enabling scalable AI deployment across the business
• Build and manage
• modern AI infrastructure
• , including Kubernetes environments, CI/CD pipelines, and infrastructure-as-code frameworks
• Develop and maintain
• platform capabilities for GenAI applications
• , including RAG architectures, model deployment, monitoring, and governance
• Establish
• engineering best practices, tooling, and platform standards
• to support AI engineers building and deploying AI solutions
• Collaborate with
• data scientists, AI engineers, and product teams
• to support the delivery of conversational AI, chatbots, and agentic AI applications
• Lead and mentor a
• team of engineers and contractors
• , ensuring strong technical direction and delivery quality across AI platform initiatives
• Contribute to the
• scaling and evolution of enterprise AI platform capabilities
• as part of the wider AI transformation programme
• Discover the Requirements:
• 10+ years of experience in
• AI engineering, machine learning engineering, or data platform engineering roles
• Strong hands-on experience building
• MLOps platforms and AI infrastructure
• using technologies such as Kubernetes, Docker, and CI/CD frameworks
• Experience building and deploying
• Generative AI applications
• , including RAG architectures, conversational AI, or agentic workflows
• Strong cloud experience with
• AWS or Azure
• , including infrastructure-as-code and scalable ML deployment environments
• Proven experience leading technical teams while remaining
• hands-on with architecture and code
• Strong understanding of
• ML lifecycle management, model deployment, monitoring, and governance
• Excellent stakeholder management skills and the ability to operate in
• fast-paced, high-impact AI environments