Principal AI Engineer

To lead the technical design, development, testing, and deployment of complex Generative AI solutions as part of the AI Engineering team, strengthening the bank s AI engineering capability through deep technical expertise, engineering excellence, and reusable solution patterns. The role is responsible for hands-on technical leadership across the AI development lifecycle, including model integration, pipeline and service design, production deployment, performance optimization, and operational readiness, ensuring that AI solutions are robust, scalable, secure, and compliant with established engineering and governance standards. Works closely with Data teams, the AI Solution Architect, and the AI Engineering Manager to translate approved solution designs and model outputs into production-grade AI systems embedded within the bank s technology ecosystem. The role also provides technical guidance on complex implementations and supports resolution of critical engineering challenges. KEY ACCOUNTABILITIES: Description Roles and Responsibilities 1. Design and implement complex, enterprise-scale AI engineering solutions, including model integration pipelines, inference services, APIs, agentic and generative AI components, and data processing workflows, in accordance with solution architecture and technical direction established with the AI Engineering Manager. 2. Lead the implementation and continuous improvement of MLOps and LLMOps practices across assigned AI solutions, including model and prompt versioning, CI/CD and CI/CT pipelines, automated testing and evaluation, observability, performance monitoring, and model and data drift detection. 3. Design scalable, resilient, secure, and reusable AI integration patterns and service components that enable AI capabilities to be consistently consumed across business applications, digital channels, and enterprise platforms. 4. Provide technical oversight and assurance for AI engineering deliverables through solution design reviews, code reviews, testing strategies, performance benchmarking, and production-readiness assessments, escalating material technical risks and decisions to the AI Engineering Manager. 5. Act as a senior technical escalation point for production AI solutions, leading the diagnosis and resolution of complex engineering issues and recommending improvements to reliability, scalability, performance, security, and cost efficiency. 6. Develop and drive the adoption of reusable AI engineering frameworks, libraries, reference implementations, accelerators, and shared tooling that improve engineering productivity, standardization, and delivery consistency across AI initiatives. 7. Collaborate with Data Science, Data Engineering, Enterprise Architecture, Cloud, Cybersecurity, and application engineering teams to translate AI requirements into robust production implementations, providing expert guidance on deployment, integration, infrastructure, scalability, and non-functional requirements. 8. Apply and promote secure, responsible, and governed AI engineering practices, ensuring solutions comply with enterprise security, data protection, responsible AI, regulatory, and technology governance requirements. 9. Evaluate emerging AI technologies, foundation models, frameworks, and engineering approaches through technical assessments and proofs of concept, providing recommendations to the AI Engineering Manager on their applicability and potential adoption. 10. Support the AI Engineering Manager in technical planning and delivery of AI initiatives by providing expert input on architecture, engineering complexity, dependencies, technical risks, and effort estimates, while mentoring AI Engineers and Senior AI Engineers and helping raise engineering standards across the team Compliance 11. 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. Policies, Processes and Procedures 12. Follow all relevant department policies, processes, standard operating procedures and instructions so that work is carried out in a controlled and consistent manner Day-to-day Operations 13. Follow the day-to-day operations related to own jobs in the department to ensure continuity of work

Skills

• Strong hands-on programming capability in Python, with the ability to independently develop, debug, and optimize AI engineering components to production quality
• Good understanding of machine learning concepts, model lifecycle management, and the operational requirements of AI systems in production environments
• Working knowledge of DevOps and LLMOps practices, including version control, CI/CD pipelines, containerization, and automated monitoring
• Analytical problem-solving skills, with the ability to diagnose and resolve engineering issues related to model performance, pipeline failures, and service integration errors
• Strong collaboration skills, with the ability to work effectively with Data, IT, architecture, and governance teams throughout the AI solution delivery lifecycle
• Good written communication skills, with the ability to produce clear technical documentation, API specifications, and deployment guides
• Structured and disciplined approach to code quality, testing, and documentation, with a commitment to engineering standards, reproducibility, and audit readiness

Desired Candidate Profile

Bachelor s degree in Computer Science, Computer Engineering, Artificial Intelligence, Software Engineering, or a related field. Principal AI Engineer 5-7 years of progressive experience in software engineering, AI engineering, or data engineering, with hands-on exposure to developing and deploying AI solutions in production environments. Solid programming proficiency in Python, with working knowledge of AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent. Experience with LLMOps tooling and practices, including containerization using Docker or Kubernetes, CI/CD pipeline configuration, model versioning, and automated testing workflows. Familiarity with LLM integration patterns, including API-based model consumption, prompt engineering, RAG pipeline development, and inference optimization techniques. Experience building and maintaining data pipelines and APIs for AI model consumption, with understanding of data transformation, feature engineering, and service integration patterns. Familiarity with enterprise security principles, access control, and audit logging requirements relevant to AI system development and production deployment.