AI Engineering Manager

JOB PURPOSE: To manage the day-to-day AI engineering function, leading a team of AI engineers responsible for the development, integration, testing, and deployment of machine learning and generative AI solutions across the bank. The role ensures engineering delivery is executed to high quality standards, within agreed timelines, and in alignment with established LLMOps (Large Language Model Operations) frameworks. The AI Engineering Manager translates approved AI solution designs into production-grade implementations, driving engineering discipline, team development, and continuous improvement of engineering practices across the full AI development lifecycle. KEY ACCOUNTABILITIES: Roles and Responsibilities 1. Oversee the engineering delivery of AI solutions from development and testing through production deployment, coordinating with MLOps/LLMOps and Technology teams for operationalization and the supporting enterprise technology environment. . 2. Ensure Implementation of LLMOps practices across the team, including model packaging, version control, CI/CD pipelines, automated testing, and model performance monitoring. 3. Collaborate with the AI Solution Architect to translate approved solution blueprints into detailed engineering plans, task breakdowns, and sprint-level delivery schedules. 4. Work with IT infrastructure, security, and DevOps teams to establish and maintain AI engineering environments, tooling, and deployment pipelines that meet enterprise security and resilience standards. 5. Manage engineering project timelines, resource allocation, and delivery risks, escalating issues to the Head of AI Engineering as appropriate and maintaining stakeholder visibility on progress. 6. Establish and uphold engineering standards, code review practices, testing frameworks, and documentation requirements to ensure output is maintainable, auditable, and reproducible. 7. Drive continuous improvement of engineering workflows, automation tooling, and reusable component libraries to increase team productivity and delivery consistency. 8. Coordinate with Enterprise Data & AI Governance and Risk functions to ensure deployed AI solutions meet explainability, monitoring, auditability, and regulatory compliance requirements. 9. Compliance 10. Comply with all relevant CBE regulations, banking laws, AML regulations and internal CIB policies and code of conduct in order to maintain CIB s sound legal position and mitigate any potential risks. Supervision 11. Supervise the activities and work of subordinates to ensure that all work within a specific area is carried out in an efficient manner and in compliance with the set policies, processes and procedures 12. Implement approved department policies, processes, and procedures and monitor adherence so that work is carried out in a controlled manner 13. Implement the day-to-day operations assigned for the AI Engineering department to ensure compliance with the established standards and procedures * Skills Proven ability to lead and develop engineering teams, setting clear objectives, managing performance, and creating a culture of technical accountability and continuous improvement. Strong hands-on technical proficiency in AI engineering, with the ability to review code, guide architectural implementation, and provide practical technical direction to engineers. Strong collaboration skills, with experience working across Data, IT, infrastructure, and governance teams to deliver integrated, compliant AI solutions. Ability to diagnose and resolve complex engineering problems related to model performance, integration failures, scalability challenges, and production reliability. Clear written and verbal communication skills, with the ability to report on engineering delivery status, technical risks, and resolution plans to management and stakeholders. Commitment to engineering quality, reproducibility, and documentation as essential foundations for sustainable and auditable AI delivery.

Desired Candidate Profile

Bachelor s degree in Computer Science, Computer Engineering, Artificial Intelligence, Software Engineering, or a related field. A Master s degree in a relevant discipline is preferred. 7+ years of progressive experience in AI/ML engineering or software engineering, with a minimum of 3 years in a team lead or engineering management capacity. Proven hands-on experience with MLOps and LLMOps frameworks, including model deployment pipelines, CI/CD tooling, automated testing, monitoring, and retraining workflows. Strong programming proficiency in Python, with working knowledge of AI/ML frameworks such as TensorFlow, PyTorch, or Scikit-learn, and familiarity with model serving and containerization technologies. Solid understanding of software engineering practices, DevOps principles, version control, and automated testing in the context of AI system development. Experience working within regulated financial services or enterprise environments, with familiarity with risk management, governance, and auditability requirements for AI systems.