Principal AI Technical Lead / AI Product Owner

SUMMARY:
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POSITION INFO:

As Principal AI Technical Lead and AI Product Owner, you will be the founding anchor of this capability. You will own the vision for how AI specifically agentic, LLM-powered systems and tooling such as Claude Code can accelerate software delivery and operational excellence across the business unit. Critically, this is not only about building a new AI team: you will be the driving force behind embedding AI-assisted delivery practices into existing engineering teams across the organisation, ensuring Claude Code, BMAD, and related tooling, methodologies and frameworks are adopted broadly and responsibly. And you will be technical enough to ensure every part of that vision is implementable, secure, and fit for a regulated environment.

What you'll do:

• Vision, Strategy & Product Ownership
  
  • Define and own the AI tooling and delivery acceleration roadmap for the business unit translating the organisation's ambition for agent-powered banking operations into a clear, sequenced, and measurable plan.
  • Identify and evaluate AI frameworks, LLM tooling, and agentic approaches relevant to financial services use cases including card issuing automation, personal lending processing, fraud detection, compliance monitoring, and customer service agents.
  • Engage business stakeholders including Risk, Compliance, Operations, and Sales to translate domain problems into AI-powered delivery outcomes with measurable ROI.
  • Define and track success metrics: developer productivity gains, processing time reductions, cycle time improvements, and quality indicators relevant to a banking context.
• Technical Leadership & Architecture
  
  • Lead the adoption of the Breakthrough Method of AI Agile Development (BMAD) as the primary framework for structuring and accelerating AI-assisted development cycles across the team.
  • Drive the evaluation and implementation of AI coding assistants and developer tooling including Claude Code and equivalent tools ensuring they are deployed with engineering rigour and appropriate access controls.
  • Architect agentic systems appropriate for banking use cases: Super Agent orchestration layers, Coordination Agents for complex multi-agent task management, and Functional Specialist agents for risk, compliance, service, and sales domains.
  • Ensure all technical decisions account for the constraints of a regulated environment: data residency, model explainability, audit trail requirements, and integration with existing core banking infrastructure.
• Embedding AI-Accelerated Delivery in Existing Engineering Teams
  
  • Act as the primary change agent for AI-assisted software delivery working hands-on with existing engineering squads to introduce and embed Claude Code, BMAD, and related tooling into their day-to-day development workflows, not just within the core AI team.
  • Design and lead a structured rollout programme for BMAD adoption across engineering teams including onboarding workshops, delivery templates, prompt engineering playbooks, and worked examples tailored to the organisation's existing tech stack and ways of working.
  • Partner with engineering leads and delivery managers to identify where AI-assisted coding, automated code review, intelligent test generation, and documentation tooling can have the greatest immediate impact on sprint velocity, quality, and developer experience.
  • Define and track cross-team adoption metrics developer productivity uplift, reduction in manual toil, cycle time improvement, and defect rate to demonstrate the value of AI-accelerated delivery to leadership and sustain investment.
• Team Building, Culture & Enablement
  
  • Help shape the hiring plan for the wider AI engineering team contributing to role design, candidate assessment, and onboarding for the full squad (Data Scientist, Software Engineers, ML Engineer, Data Engineer/Analyst).
  • Act as a mentor and technical multiplier upskilling engineers in AI tooling, BMAD methodology, agentic patterns, and responsible AI practices specific to a financial services context.
  • Build an internal culture of experimentation balanced with regulatory discipline: move fast enough to stay relevant but build things that can withstand audit.
  • Create and maintain internal playbooks, documentation, and training materials that enable squads beyond the core team to adopt AI tooling safely and effectively.

• Enterprise AI Tool Guardrails & Security Standards
  • Define approved usage patterns for AI coding assistants in a banking-grade engineering environment specifying what codebases and data they may access, what AI-generated outputs require mandatory human review before merge, and what constitutes acceptable AI assistance at each stage of the SDLC.
• Team Building, Culture & Enablement
  
  • Help shape the hiring plan for the wider AI engineering team contributing to role design, candidate assessment, and onboarding for the full squad (Data Scientist, Software Engineers, ML Engineer, Data Engineer/Analyst).
  • Act as a mentor and technical multiplier upskilling engineers in AI tooling, BMAD methodology, agentic patterns, and responsible AI practices specific to a financial services context.
  • Build an internal culture of experimentation balanced with regulatory discipline: move fast enough to stay relevant but build things that can withstand audit.
  • Create and maintain internal playbooks, documentation, and training materials that enable squads beyond the core team to adopt AI tooling safely and effectively.

Your Expertise:

• 8+ years of experience in software engineering, with a meaningful portion in principal-level or technical leadership roles.
• Proven, hands-on experience with LLM-powered developer tooling including direct experience with Claude Code or equivalent AI coding assistants in production or near-production environments.
• Practical experience with agentic AI systems and multi-agent architectures including Super Agent / orchestration layers, manager agents, and functional specialist agent patterns.
• Experience with or deep understanding of the Breakthrough Method of AI Agile Development (BMAD) and its application to accelerating software delivery.
• Track record of owning a product or technical roadmap from vision through to delivered outcomes, including stakeholder management across business and technical audiences.