Head of AI Engineering

JOB PURPOSE To lead the enterprise AI Engineering function and build a secure, scalable, and production-ready AI capability across the bank. The role is responsible for designing and operationalizing the end-to-end AI engineering architecture, including LLMOps (Large Language Model Operations) frameworks, to ensure AI solutions are reliably developed, deployed, monitored, and maintained in line with regulatory and risk requirements.

The Head of AI Engineering bridges data, technology, and business teams to translate AI use cases into resilient, enterprise-grade solutions embedded in organizational processes. The role ensures AI solutions are robust, auditable, and aligned with enterprise architecture standards, enabling sustainable value creation while maintaining strong governance and control.

KEY ACCOUNTABILITIES

1. Define and lead the enterprise AI Engineering vision to accelerate the development, deployment, and scaling of Generative AI solutions across the bank. 2. Develop the long-term AI Engineering and platform strategy, including AI architecture, LLMOps (Large Language Model Operations), model lifecycle management, and automation standards. 3. Develop, maintain, and deliver a prioritized enterprise AI use case roadmap aligned with business strategy, clearly linking each initiative to measurable value creation, productivity gains, risk reduction, or revenue enhancement, and tracking realization of expected benefits post-deployment. 4. Work closely with Data, IT, Risk, and business teams to translate AI use cases into scalable, production-grade solutions embedded in the bank’s systems and processes. 5. Coordinate with Enterprise Data & AI Governance and Risk functions to ensure AI models comply with governance, regulatory, explainability, and risk management requirements. 6. Collaborate with enterprise architecture and Technology teams to ensure AI solutions are designed and deployed within approved enterprise technology, infrastructure, security, and architecture standards.. 7. Lead and oversee AI engineering programs, ensuring AI solutions are properly engineered, tested, validated, deployed, and monitored within agreed timelines. 8. Define and implement AI engineering standards, reusable engineering components, development practices, and deployment approaches for AI solutions, in alignment with enterprise technology standards and the MLOps/LLMOps operating model. 9. Scale AI engineering practices, workflows, and tooling to improve automation, reproducibility, performance monitoring, and continuous integration and deployment. 10. Identify, assess, and mitigate technical and operational risks related to AI deployment, including performance degradation, bias, drift, security, and resilience. 11. Establish and govern the end-to-end AI engineering lifecycle practices, including solution development, engineering, testing, deployment, versioning, and technical monitoring, in coordination with MLOps/LLMOps and applicable governance requirements 12. Establish monitoring and observability frameworks to ensure ongoing reliability, transparency, and auditability of AI solutions in production. 13. Oversee the AI engineering lifecycle from approved solution design through engineering, testing, and production deployment, coordinating with MLOps/LLMOps for operational lifecycle management and with Technology for underlying enterprise technology services. 14. Lead and contribute to the formulation and execution of the AI Engineering strategy, ensuring full alignment with CIB’s overall strategic objectives, technology roadmap, and enterprise architecture principles. Policies, Processes and Procedures 15. Participate and recommend improvements to policies, processes and procedures and manages their implementation to ensure all relevant procedural / legislative requirements are fulfilled. Day-to-day management 16. Supervise the day to day operations of the department providing guidance in the related area, encouraging teamwork and facilitating related professional work processes in order to achieve high performance standards. 17. 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. 18. Ensure compliance 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.

Qualifications & Experience  Bachelor’s degree in Computer Science, Computer Engineering, Artificial Intelligence, Information Systems, or a related field. A Master’s degree in AI, Machine Learning, or a relevant discipline is preferred.  10+ years of progressive experience in AI engineering, machine learning engineering, or advanced analytics platforms, with proven experience deploying AI solutions into production environments.  Minimum 5 years of experience leading and developing high-performing engineering teams, including AI engineers, ML engineers, or platform specialists.  Proven hands-on experience with LLMOps frameworks, including CI/CD pipelines, automated testing, monitoring, retraining, and performance management.  Extensive experience building and optimizing large-scale data and AI pipelines using distributed processing and streaming technologies such as Spark, Kafka, and similar big data frameworks.  Strong programming expertise in Python is essential; experience with additional languages and AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent is required.  Solid understanding of enterprise architecture, security controls, and infrastructure principles relevant to deploying AI solutions in financial services environments.

Skills  Proven ability to build, lead, and develop high-performing AI engineering teams, fostering a culture of accountability, quality, and continuous improvement.  Strong ability to communicate complex AI and technical concepts clearly to non-technical stakeholders, executive management, and governance forums.  Advanced problem-solving skills with a structured approach to diagnosing performance, scalability, security, and operational risks in AI systems.  Results-oriented mindset with strong focus on business value realization and operational reliability.  Engineering mindset with deep understanding of software development practices, DevOps, CI/CD pipelines, and automation.  Strong programming capability, particularly in Python, with solid understanding of machine learning frameworks and model serving technologies.  Strong understanding of large-scale data processing, distributed systems, APIs, and integration patterns required for embedding AI into enterprise systems.