Manager Machine Learning Operations and Artificial Intelligence Engineering job at CRDB

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.

Principle 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.
• Establish reusable engineering practices for code review, testing, version control, integration, deployment and documentation within bank standards.
• Oversee platform and solution designs for scalability, security and maintainability; route architecture decisions through the appropriate authority.
• Ensure release packages include reproducible artefacts, test results, support ownership, monitoring, rollback and required validation and approval evidence.
• Coordinate controlled deployment and material changes; prevent delivery pressure from bypassing governance, security or change-management conditions.
• Agree service objectives and escalation routes with ICT and business owners; oversee availability, latency, failures, capacity and recovery performance.
• Lead engineering incident response within assigned responsibilities, coordinating restoration, communication, root-cause review and corrective actions.
• Oversee platform access, patching, vulnerability remediation, backup and recovery testing with responsible ICT and Cybersecurity teams.
• Ensure model-quality and drift alerts reach Model Owners and Data Scientists; route retraining and material changes through validation and approval.
• Plan infrastructure, licensing and supplier-support needs; monitor approved expenditure and consumption and escalate budget or capacity constraints.
• Coordinate technical dependencies and delivery forecasts with the AI Portfolio Delivery manager; maintain realistic commitments and early issue escalation.
• Report engineering quality, service reliability, release failures, recovery time and costs; maintain evidence for assurance and improve delivery practices.
• 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.
• Oversee the design, implementation, and management of automated deployment pipelines, infrastructure-as-code, environment provisioning, and production support processes.
• Collaborate with ICT Infrastructure, Enterprise Architecture, Cybersecurity, Data Engineering, Data Science, and business stakeholders to ensure enterprise-wide alignment of AI platforms and services.
• Monitor platform performance, resource utilization, operational resilience, and service availability; drive proactive remediation and continuous improvement initiatives.

Qualifications Required

• Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Data Engineering, Cloud Computing, Information Systems, Information Technology, Cybersecurity, or a related field.
• A Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Cloud Computing, Information Systems, Technology Management, Business Administration (MBA), or a related field will be an added advantage.
• Minimum of 7 years of relevant software engineering, platform engineering, cloud engineering, infrastructure engineering, MLOps, or production operations experience supporting enterprise technology solutions.
• Minimum of 2 years of experience leading technical teams, managing technical delivery, supervising engineering staff, or coordinating complex technology initiatives.
• Relevant cloud, DevOps, architecture or service-management certification is preferred.
• Experience taking AI or data solutions into supported production services with controlled release and recovery processes.
• Knowledge of software engineering, ML operations, platform architecture, security controls and service management.
• Strong resource planning, incident leadership, supplier coordination and stakeholder communication skills.
• Experience leading software engineering, MLOps, platform engineering, cloud infrastructure, or AI engineering teams supporting enterprise-scale technology solutions.
• Knowledge of Artificial Intelligence, Machine Learning lifecycle management, MLOps practices, platform engineering principles, and AI governance requirements.

• 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.
• Establish reusable engineering practices for code review, testing, version control, integration, deployment and documentation within bank standards.
• Oversee platform and solution designs for scalability, security and maintainability; route architecture decisions through the appropriate authority.
• Ensure release packages include reproducible artefacts, test results, support ownership, monitoring, rollback and required validation and approval evidence.
• Coordinate controlled deployment and material changes; prevent delivery pressure from bypassing governance, security or change-management conditions.
• Agree service objectives and escalation routes with ICT and business owners; oversee availability, latency, failures, capacity and recovery performance.
• Lead engineering incident response within assigned responsibilities, coordinating restoration, communication, root-cause review and corrective actions.
• Oversee platform access, patching, vulnerability remediation, backup and recovery testing with responsible ICT and Cybersecurity teams.
• Ensure model-quality and drift alerts reach Model Owners and Data Scientists; route retraining and material changes through validation and approval.
• Plan infrastructure, licensing and supplier-support needs; monitor approved expenditure and consumption and escalate budget or capacity constraints.
• Coordinate technical dependencies and delivery forecasts with the AI Portfolio Delivery manager; maintain realistic commitments and early issue escalation.
• Report engineering quality, service reliability, release failures, recovery time and costs; maintain evidence for assurance and improve delivery practices.
• 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.
• Oversee the design, implementation, and management of automated deployment pipelines, infrastructure-as-code, environment provisioning, and production support processes.
• Collaborate with ICT Infrastructure, Enterprise Architecture, Cybersecurity, Data Engineering, Data Science, and business stakeholders to ensure enterprise-wide alignment of AI platforms and services.
• Monitor platform performance, resource utilization, operational resilience, and service availability; drive proactive remediation and continuous improvement initiatives.

• software engineering
• ML operations
• platform architecture
• security controls
• service management
• resource planning
• incident leadership
• supplier coordination
• stakeholder communication
• Artificial Intelligence
• Machine Learning lifecycle management
• MLOps practices
• platform engineering principles
• AI governance requirements

• Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Data Engineering, Cloud Computing, Information Systems, Information Technology, Cybersecurity, or a related field.
• A Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, Cloud Computing, Information Systems, Technology Management, Business Administration (MBA), or a related field will be an added advantage.
• Minimum of 7 years of relevant software engineering, platform engineering, cloud engineering, infrastructure engineering, MLOps, or production operations experience supporting enterprise technology solutions.
• Minimum of 2 years of experience leading technical teams, managing technical delivery, supervising engineering staff, or coordinating complex technology initiatives.
• Relevant cloud, DevOps, architecture or service-management certification is preferred.
• Experience taking AI or data solutions into supported production services with controlled release and recovery processes.