MANAGER MACHINE LEARNING OPERATIONS AND ARTIFICIAL INTELLIGENCE ENGINEERING
Job Description
Job purpose
The Manager, ML Ops & AI Engineering leads ML Engineers, ML Ops Engineers and AI Platform Engineers to deliver reliable AI services. The role directs technical delivery, platform operations and production support, manages staff performance and service risks, and coordinates with ICT, Cybersecurity, Data Science and portfolio delivery.
Principal responsibilities
Translate the approved AI roadmap into engineering and operational plans with priorities, accountable owners, capacity needs and support commitments.
Direct ML Engineers, ML Ops Engineers and AI Platform Engineers; allocate work, review performance, coach staff and address skills gaps and key-person dependencies.
Define responsibility boundaries and handovers with ICT, Cybersecurity, Data Science and business owners for shared platforms and deployed solutions.
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Establish reusable engineering practices for code review, testing, version control, integration, deployment and documentation within bank standards.
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Oversee platform and solution designs for scalability, security and maintainability; route architecture decisions through the appropriate authority.
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Ensure release packages include reproducible artefacts, test results, support ownership, monitoring, rollback and required validation and approval evidence.
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Coordinate controlled deployment and material changes; prevent delivery pressure from bypassing governance, security or change-management conditions.
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Agree service objectives and escalation routes with ICT and business owners; oversee availability, latency, failures, capacity and recovery performance.
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Lead engineering incident response within assigned responsibilities, coordinating restoration, communication, root-cause review and corrective actions.
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Oversee platform access, patching, vulnerability remediation, backup and recovery testing with responsible ICT and Cybersecurity teams.
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Ensure model-quality and drift alerts reach Model Owners and Data Scientists; route retraining and material changes through validation and approval.
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Plan infrastructure, licensing and supplier-support needs; monitor approved expenditure and consumption and escalate budget or capacity constraints.
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Coordinate technical dependencies and delivery forecasts with the AI Portfolio Delivery manager; maintain realistic commitments and early issue escalation.
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Report engineering quality, service reliability, release failures, recovery time and costs; maintain evidence for assurance and improve delivery practices.
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Lead the implementation and continuous improvement of MLOps, DevOps, platform engineering, and software engineering practices to support secure, scalable, and reliable AI solutions across the Bank.
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