ROLE OVERVIEW
PURPOSE OF JOB
As JTC enters the Genesis era of growth and transformation under private equity ownership, the Group’s ambition is to become a data- and AI-powered specialist-services platform where every interaction, acquisition and piece of expertise makes the firm smarter, more efficient and easier to scale. The Director, AI Engineering leads the AI Engineering & Data Science function within the unified Tech-Ops operating model, the engineering engine room that turns JTC’s AI & Agentic Strategy 2030 into working, governed and value-generating systems.
The postholder will build and lead the team responsible for AI/ML model development and agentic engineering across the three pillars of the AI strategy; AI for Service Delivery, AI for Digital Products and AI for Productivity. This spans the JTC Agentic Platform (MCP-enabled orchestration and validation across JTC systems and market tools), the enterprise document extraction platform, end-to-end billing agents (Agentic Thames and the Agentic Wheel), conversational AI analytics on the Klondike data platform, function-specific JTC Agents and the Group-wide Agent Builder; all delivered on a consolidated, Claude-based AI stack that preserves in-house control and avoids vendor lock-in.
This is a hands-on engineering leadership role. Acting as the Group’s hub for AI in the Businesses and Group functions, the postholder pairs deep technical credibility with commercial judgement: every model and agent shipped must deliver measurable operational ROI against the Technology Value Creation Plan, operate within the AI Governance & Model Risk framework, and stay cost-disciplined through FinOps controls. The ideal candidate combines frontier LLM and agentic engineering expertise with the leadership skills to build and scale a high-performing team in a PE-backed environment.
MAIN RESPONSIBILITIES AND DUTIES
AI & Agentic Engineering Delivery
- Lead the design, development and production deployment of AI/ML models and agentic systems across the Group, delivering the prioritised use cases on JTC’s two-year roadmap (55 use cases selected on value, feasibility and strategic fit) to enterprise quality.
- Build and scale the near-term delivery priorities: Agentic Thames (the end-to-end billing agent combining contract-reading, scoping, billing and timesheet quality-assurance AI skills), the Agentic Wheel billing transformation, AI-enabled tax workflow redesign, the standardised AI assistant, and AI-enabled analytics on the Klondike financial reporting platform.
- Engineer Digital Integrated Agents for JTC’s client-facing digital products, enabling non-linear user journeys, content and dynamic UI generation, conversational AI-enabled analytics and reporting, and persona-aware hyper-personalisation services.
- Deliver the enterprise document extraction platform and agent supporting workflows across service delivery, driving timeliness, accuracy and efficiency in middle- and back-office operations.
- Ship function-specific JTC Agents (e.g. Billing, Data Quality Control), individual AI productivity assistants with secure access to JTC data and systems and institutionalise the multi-platform Agent Builder for craft-your-own agents.
Agentic Platform & AI Architecture
- Own the engineering of the JTC Agentic Platform, MCP-enabled orchestration and validation across JTC systems and market tools, maintaining in-house control of the agentic layer to avoid LLM lock-in.
- Build around the consolidated Claude stack (Claude, Cowork, Claude Code and the developer platform), migrating existing agentic workloads onto it and prioritising app-agnostic agents on the JTC Agentic Layer over vendor-embedded AI.
- Partner with Data Engineering & Provisioning to ensure AI-ready data, reusable, governed structured and unstructured data pipelines feeding models, agents and analytics across the Group.
- Define and enforce AI engineering standards, deployment patterns, integration patterns, testing and evaluation frameworks, and contribute AI/data positions to the Architecture Forum’s buy-vs-build and standards decisions.
- Evaluate third-party embedded AI and SaaS agents (e.g. Safebooks.ai, Rossum) for benefit and cross-application scalability, integrating those that earn their place on the platform.
Prioritisation & Value Delivery
- Co-own use-case prioritisation with the AI Product Management function, impact and feasibility assessment, quarterly roadmap refresh, dependency and sequencing management, and capacity and investment planning across the AI portfolio.
- Embed measurable operational ROI in every AI deployment and support the VCP PMO’s monthly delivery and value reviews, portfolio reporting and benefit realisation tracking for the Enterprise AI & Knowledge and related initiative clusters.
- Work with AI Product Managers who sit with users to convert real business requirements into engineered agents, iterating rapidly from pilot to production based on user feedback and delivered value.
- Translate engineering progress into business and financial narratives for the Executive Committee, Steerco and PE sponsor reporting cadences.
AI Governance, Model Risk & Fin Ops
- Co-develop and operate within the AI Governance & Model Risk framework, model risk and assurance, responsible-AI policy, audit trail and human-in-the-loop controls, and support the AI Governance Forum’s oversight of fairness, explainability and control of AI use cases.
- Embed security, resilience and regulatory control by design in every AI system, working with Cyber & Infrastructure to scale controls for data and AI initiatives rather than bolting them on late.
- Apply FinOps discipline to AI at scale, AI/token and cloud cost management, usage monitoring and unit-economics control to stay ahead of run-cost.
- Ensure AI systems are auditable and evidenced for regulators, clients and internal risk functions across JTC’s regulated, multi-jurisdiction environment.
Team Leadership & Capability Building
- Build, lead and develop the AI Engineering & Data Science team, hiring targeted internal capability in AI Engineering and AI Modelling, with phased headcount growth aligned to investment and business case.
- Act as the Group’s hub for AI in the Businesses and Group functions, upskilling Tech-Ops in AI and agentic workflow delivery and championing adoption of coding assistants (Claude Code) across engineering.
- Represent JTC AI engineering in external forums, vendor engagements and PE portfolio technology networks.
- Continuously horizon-scan the frontier of model and agent development, translating advances into practical opportunity for JTC’s AI roadmap.
ESSENTIAL REQUIREMENTS
- Candidates should bring demonstrable progressive experience across AI/ML engineering, data science and enterprise software delivery; including leading engineering teams, ideally in Financial Services, Professional Services or PE-backed environments.
- Demonstrable track record shipping production-grade AI/ML and LLM-based systems at enterprise scale from prototype through governed production deployment and ongoing operation.
- Deep hands-on expertise in LLMs and agentic systems: prompt and context engineering, retrieval-augmented generation, tool/MCP orchestration, multi-agent workflows and evaluation frameworks. Experience with the Anthropic stack (Claude, Claude Code, agent SDKs) is strongly advantageous.
- Strong software engineering foundations; APIs, cloud platforms, CI/CD, data pipelines and experience integrating AI into core enterprise systems and business workflows.
- Proven experience building document intelligence/extraction, workflow automation or conversational analytics solutions in control-heavy, regulated environments where specialists retain oversight and judgement.
- Working knowledge of AI governance and model risk: responsible-AI policy, explainability, human-in-the-loop controls, audit trails and model assurance.
- FinOps awareness; token and cloud cost management, usage monitoring and the unit economics of running AI at scale.
- Experience partnering with product management to prioritise use cases by value and feasibility, and to measure and report realised benefit against investment cases.
- Proven leadership of engineering or data science teams; hiring, developing and retaining talent in a scaling environment with the executive presence to represent AI engineering at senior leadership level.
- Sector experience in trust, fund administration, wealth management, asset management, or professional financial services is highly advantageous.
- A relevant degree in computer science, machine learning or a related discipline, and relevant professional certifications, are valued but not required.
OUR COMMITMENT TO INCLUSION & WELLBEING
JTC is committed to fostering a healthy, inclusive organisation where all individuals feel welcome and feel able to participate in the workplace fully. We value different perspectives, backgrounds and lived experiences. This includes supporting employee wellbeing so that people feel equipped to thrive.
