Applied AI Product Director

J.P. Morgan Personal Investing offers award-winning investments, products and digital wealth management services to over 275,000 investors in the UK. We built the business with innovation as a core part of our ethos to give consumers the confidence and clarity to make informed investment decisions and achieve their financial goals. Our Product team is at the heart of this venture, focused on getting smart ideas into the hands of our teams and customers. Our teams are solution-oriented, commercially savvy, and have a head for fintech. We work in tribes and squads that focus on specific products and projects — and depending on your strengths and interests, you'll have the opportunity to move between them.

As an Applied AI Product Director at JPMorganChase within J.P. Morgan Personal Investing (JPM PI), you will own the product vision, strategy, and roadmap for AI-powered solutions across the business spanning large language models, agentic systems, intelligent automation, AI assistants, and rapid prototyping infrastructure. This is not a role that simply manages a backlog. You will define what AI can do for JPM PI, translate emerging capabilities into applied tools that deliver measurable results, and lead cross-functional teams to build and deploy them at scale.

You will collaborate closely with Data Scientists, Software Engineers, other product teams, and business stakeholders to deliver impactful AI solutions from NLP-powered insights and automation platforms to self-serve AI enablement that empowers every team to build better product features and deliver richer customer experiences. You will also shape our AI integration strategy, developing a clear point of view on how agentic workflows, emerging standards, and third-party AI tooling are changing what's possible, and making the build-versus-partner-versus-defer calls that keep us moving at the right pace. This role requires a leader who delivers measurable results for sophisticated AI products under ambiguous conditions in a fast-paced environment someone who is as comfortable discussing an API spec or an auth flow with an engineering team as they are presenting an AI strategy to senior management. You will thrives in collaborative squads, are passionate about the transformative potential of AI, and have the conviction to define what "great" looks like as the landscape evolves.

Job responsibilities:

• Develops and articulates a clear Applied AI product strategy and roadmap that delivers meaningful value across JPM PI, covering LLMs, agentic capabilities, AI assistants, intelligent automation, and rapid prototyping, while setting team goals, success metrics, and priorities aligned with broader business objectives.
• Leads cross-functional teams of Data Scientists, Software Engineers, and Platform teams to move AI solutions from concept to production at scale, making decisions on AI tooling and capabilities to keep delivery moving at the right pace.
• Turns ambiguous demand into prioritised, validated AI use cases with clear production pathways through measurable experiments, delivering results for sophisticated AI products under conditions of genuine uncertainty.
• Identifies where autonomous agents, multi-step reasoning, tool-using architectures, and emerging integration standards create step-change opportunities across the business, evaluating internal and third-party AI platforms AI to ensure JPM PI stays at the frontier without creating ungoverned dependencies.
• Empowers teams across JPM PI to safely leverage JPMC AI tools through repeatable workflows, reusable skills, practical guardrails, and structured training, building and running the framework for how we tier, prioritise, and resource AI use-case requests across multiple teams and functions.
• Works closely with data teams to surface actionable insights, shape AI-ready datasets, identify data constraints and quality gaps, and unlock new product features through LLMs and NLP.
• Deploys LLM and agentic capabilities to improve engineering quality, delivery speed, and operational resilience, covering automated code review, standards enforcement, context-aware development assistance, copilot-delivered boilerplates, root-cause analysis, incident diagnosis, and system audits that surface technical debt, delivering measurable reductions in rework and overhead.
• Leverages internal AI platforms and prototyping infrastructure to move from idea to working prototype at speed, then partners with Product and Engineering to convert prototypes into production-grade solutions.
• Leads change impact assessments for new or modified AI products, owns AI risk assessments and related reporting, and contributes to the definition and ongoing review of the Risk Appetite statement for applied AI capabilities.
• Builds strong, collaborative relationships across departments, including Product Managers, other product teams, Platform teams, Engineering, Data Science, and all AI functions, scaling reusable AI skills and agents across the business while navigating regulatory and compliance realities.

Required qualifications, capabilities and skills

• Extensive product management experience or equivalent expertise, including extensive experience delivering AI/ML-powered products, AI platforms, or developer tools in financial services or technology companies, and expert at navigating matrix and complex organisations, collaborating effectively across teams and functions including Data Science, Platform Engineering, Model Risk, and AI governance bodies.
• Deep working knowledge of GenAI/LLMs, agentic systems, RAG architecture, and AI-assisted software development, with demonstrated ability to translate technical AI capabilities into applied, value-added tools for non-technical users.
• Proven track record of owning AI product vision and roadmap end-to-end, and leading cross-functional teams that deliver enterprise-scale AI/ML solutions with measurable outcomes, including AI assistants, intelligent automation, AI-native workflows, or developer productivity tools.
• Strong understanding of AI/ML model lifecycle management, including evaluation, deployment, monitoring, and governance. Practical experience embedding AI into business operations and engineering workflows.
• Genuinely technical and a proven builder. Comfortable as a hands-on partner with engineering and data science teams, able to reason through API design, system architecture, and technical trade-offs, with credibility to lead build-versus-partner-versus-defer decisions.
• Proven experience identifying, validating, and prioritising AI use cases through measurable experiments, delivering results for sophisticated products under ambiguous conditions in fast-paced environments.
• Experience building self-serve AI enablement programmes, including training, guardrails, reusable templates, prioritisation frameworks, and adoption measurement for non-engineering teams.
• Deep understanding of the evolving AI regulatory and governance landscape, with familiarity across responsible AI principles, model risk management, and emerging frameworks for AI deployment in financial services.
• A natural connector who creates shared understanding by translating AI concepts, technical trade-offs, and business priorities across Product, Engineering, Data Science, Compliance, Risk, and non-technical functions. Can flex between a technical design conversation with engineers and a strategic conversation with an executive.

• Comfortable with ambiguity. This role sits at the intersection of rapidly evolving AI capabilities and established financial services governance, and part of the job is defining what "great" looks like as you go.

Preferred qualifications, capabilities and skills

• Bachelor's degree or equivalent. MSc in Data Science/AI/ML, or relevant advanced degree advantageous.
• Experience with AI governance frameworks, model risk management, and responsible AI principles in financial services.
• Practical understanding of data governance, data privacy, and regulatory requirements as they apply to AI/ML products in financial services.
• Vendor management experience including build-versus-buy analysis and integration strategy for third-party AI/ML solutions (e.g., LLM providers, copilot platforms, AI orchestration frameworks, or agentic tooling), ideally including evaluating AI-native vendors at various maturity stages.
• Familiarity with current AI industry developments, including agentic AI systems, emerging agent-to-agent integration standards (e.g., MCP), AI-assisted software development (e.g., Claude Code, GitHub Copilot), frontier model capabilities, and evolving best practices for embedding AI into enterprise workflows.
• Experience working with internal AI platforms, innovation labs, or rapid prototyping functions to move from concept to validated prototype at speed.