Director – AI Engineering

Overview

In this role you will lead the AI Engineering & Data Science function within a PE-backed group, building and scaling AI/ML models and agentic systems that power service delivery, digital products and productivity. You will own the JTC Agentic Platform and Claude-based stack, ensuring measurable ROI and governance while maintaining cost discipline. You will partner with product, data and security teams to translate business needs into production-ready AI solutions. This leadership role combines deep technical credibility with commercial judgement to drive enterprise-scale impact.

Responsibilities

• Lead design, development and production deployment of AI/ML models and agentic systems on the two-year roadmap (55 use cases)
• Build and scale near-term delivery priorities: Agentic Thames, Agentic Wheel, AI-enabled tax workflow redesign, standardised AI assistant, AI analytics on Klondike
• Develop Digital Integrated Agents for client-facing products and hyper-personalisation services
• Deliver enterprise document extraction platform and agent workflows to improve middle/back-office efficiency
• Ship function-specific JTC Agents and enable the multi-platform Agent Builder
• Own engineering of the JTC Agentic Platform, MCP-enabled orchestration and in-house control to avoid LLM lock-in
• Migrate workloads to Claude stack and prioritise app-agnostic agents
• Collaborate with Data Engineering to ensure AI-ready data pipelines
• Define AI engineering standards, deployment patterns, testing frameworks and standards decisions
• Evaluate third-party embedded AI/SaaS agents for integration and scalability
• Co-own use-case prioritisation with AI Product Management and drive roadmap and value delivery
• Work with AI Product Managers to translate business requirements into engineered agents and iterate pilots to production
• Translate engineering progress into business and financial narratives for leadership and sponsor reporting
• Co-develop and operate within AI Governance & Model Risk framework and responsible-AI policy
• Embed security, resilience and regulatory control by design; scale controls with Cyber & Infra
• Apply FinOps discipline for AI at scale and monitor unit economics of AI runs
• Ensure auditable systems for regulators and risk functions
• Lead and develop the AI Engineering & Data Science team with phased headcount growth
• Upskill Tech-Ops in AI and champion adoption of coding assistants across engineering
• Represent JTC AI engineering in external forums and vendor engagements
• Continuously horizon-scan frontier of model and agent development to identify practical opportunities

Key requirements

• Progressive experience in AI/ML engineering, data science and enterprise software delivery
• Proven track record shipping production-grade AI/ML and LLM-based systems at enterprise scale
• Deep hands-on expertise in LLMs and agentic systems: prompt engineering, retrieval-augmented generation, tool/MCP orchestration, multi-agent workflows
• Experience with Anthropic stack (Claude, Claude Code, agent SDKs) is advantageous
• Strong software engineering foundations: APIs, cloud, CI/CD, data pipelines, integration into core enterprise systems
• Experience building document intelligence/extraction, workflow automation or conversational analytics in regulated environments
• Knowledge of AI governance, model risk, explainability, human-in-the-loop controls and audit trails
• FinOps awareness: token and cloud cost management, usage monitoring, unit economics
• Experience partnering with product management to prioritise use cases by value/feasibility and measure investment benefits
• Proven leadership of engineering or data science teams in scaling environments; executive presence
• Sector experience in trust, fund administration, wealth/asset management or professional financial services advantageous
• Relevant degree in computer science, ML or related discipline; certifications valued but not required

• leadership and people development
• commercial judgement
• stakeholder management
• AI/ML engineering
• LLM and agentic systems
• prompt/context engineering

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