Principal, Engineering, AI

Accountabilities & Responsibilities 

The Principal, AI/ML Engineer leads the design, delivery and continuous improvement of AI and ML solutions within their area of responsibility, aligning engineering activities to the Bank’s broader AI strategy and long-term objectives. The role combines strong technical leadership with a clear focus on delivery outcomes, ensuring solutions are scalable, reliable and aligned to business value.

The Principal operates with a clear understanding of the wider technology landscape, contributing to strategic direction while taking accountability for translating this into practical, high-quality engineering outcomes across squads.

• Leads the definition and execution of AI/ML engineering direction across one or more squads, ensuring alignment with the Bank’s AI strategic vision, as set out by the AI Capability Lead, and architectural principles.
• Accountable for shaping and evolving technical approaches for AI/ML systems within their area, ensuring solutions are consistent with agreed patterns, standards and platforms.
• Contributes to the development and adoption of reference architectures for key AI components, including LLMs, vector search and inference services, ensuring solutions are scalable and maintainable.
• Leads technical discovery activities such as proofs of concept, feasibility assessments and vendor evaluations, translating findings into clear recommendations and delivery plans.
• Supports design reviews and technical checkpoints for AI initiatives, ensuring risks are identified early and responsible AI considerations such as fairness, privacy and safety are incorporated into delivery.
• Guides squads in adopting best practices in observability, incident management and service-level design to ensure reliable and resilient AI/ML services.
• Works closely with Product Owners and Platform teams to prioritise and sequence backlogs, balancing delivery of business value with ongoing improvement and technical sustainability.
• Mentors engineers within and across squads, contributing to the development of capability in data and ML engineering and fostering knowledge sharing.
• Engages with senior stakeholders to communicate technical direction, delivery progress and key risks in a clear and structured manner.
• Maintains an understanding of commodity AI solutions and ensures appropriate security and governance guardrails are applied when introducing new capabilities.

Software Design and Development 

• Leads the design and delivery of AI/ML solutions within their scope, ensuring high standards of performance, scalability and maintainability.
• Designs and oversees implementation of end-to-end ML workflows, including data pipelines, feature engineering, model development and deployment, working closely with AI architecture roles to ensure alignment with enterprise patterns and standards.
• Oversees the end-to-end AI/ML lifecycle within their scope, from prototyping through to deployment and optimisation.
• Ensures that solutions consider performance, interpretability and appropriate use of AI, in line with organisational standards and expectations.
• Promotes the adoption of modern AI techniques, including machine learning, natural language processing and advanced analytics, where appropriate to business needs.
• Champions effective software engineering practices, including modular design, reuse of components and adherence to coding standards.
• Identifies and drives opportunities to enhance existing solutions or introduce innovation through the application of emerging technologies.

Quality Assurance 

• Defines and embeds appropriate testing and validation approaches for AI/ML solutions, including model evaluation and performance testing.
• Ensures that monitoring and benchmarking practices are in place so that models perform reliably and consistently in production.
• Supports the implementation of processes to identify and address model drift, data issues and potential bias.
• Promotes a culture of quality within squads, ensuring solutions meet business requirements and relevant regulatory standards.
• Represents the team in internal and selected external technical forums where appropriate, contributing to knowledge sharing and continuous improvement.

Operations, Maintenance, Support and Documentation 

• Leads the implementation of MLOps practices within their area, supporting efficient model deployment, monitoring and lifecycle management.
• Ensures that CI/CD practices are applied to AI/ML workflows to improve delivery speed and reliability.
• Oversees the ongoing performance of production AI systems within their scope, addressing issues related to scalability and stability.
• Ensures that appropriate documentation is in place for models, datasets and key technical decisions, supporting maintainability and transparency.

Data and Architecture 

• Contributes to the evolution of AI/ML architecture within their area, ensuring solutions are scalable, efficient and aligned with enterprise direction.
• Guides the optimisation of data pipelines, feature stores and model serving approaches to support effective AI delivery.
• Supports the evaluation and adoption of cloud-based AI/ML services, ensuring choices are aligned with technical and business requirements.
• Ensures that ethical AI principles and security considerations are embedded in solution design and implementation.

Knowledge, Skills, Experience & Qualifications