Senior AI Product Manager - AI and Agentic Product Strategy

📍 Job Overview

Job Title: Senior AI Product Manager - AI and Agentic Product Strategy

Company: JPMorgan Chase & Co.

Location: London, England, United Kingdom

Job Type: Full time

Category: Product Management / AI & Machine Learning Operations

Date Posted: 2026-09-07

Experience Level: 5-10 years

Remote Status: On-site

🚀 Role Summary

• Lead product strategy and delivery for AI-driven products and autonomous agents to automate critical operational challenges within a global financial institution.

• Translate complex business needs into actionable product roadmaps and define requirements for leveraging cutting-edge technologies such as LLMs, agentic orchestration, and ML platforms.

• Drive end-to-end product lifecycle management for AI-enabled solutions, from ideation and technical requirements to execution, launch, and iterative improvement.

• Foster strong cross-functional collaboration with engineering, design, analytics, and business leads to ensure successful product development and deployment in a large, matrixed organization.

• Focus on delivering measurable business outcomes and operational efficiencies by identifying high-impact AI use cases and ensuring robust evaluation and iteration for production-quality solutions.

📝 Enhancement Note: While the title is "Product Manager," the emphasis on AI, agentic services, LLMs, and operational challenges firmly places this role within the operational technology and GTM strategy domain, specifically focusing on the implementation and impact of AI within business processes. The "operations" aspect is inherent in driving efficiency and automation within banking functions.

📈 Primary Responsibilities

• Identify and prioritize high-impact use cases for AI and agentic services that demonstrably improve key performance indicators (KPIs) within Corporate and Investment Banking operations.

• Own the complete product strategy, including defining the product vision, roadmap, and detailed product requirements documents (PRDs) that enable robust platform capabilities.

• Lead and motivate cross-functional project teams, comprising engineers, designers, analysts, and business stakeholders, guiding them through complex technical and non-technical decisions to successful product launches.

• Develop and execute comprehensive project plans, meticulously incorporating technical requirements, accurate resource estimates, and realistic timelines to ensure timely delivery.

• Effectively communicate progress, risks, and strategic decisions to senior stakeholders, securing buy-in, managing expectations, and proactively unblocking critical decisions.

• Design and implement robust evaluation sets for AI models, conducting quantitative and qualitative iterations to achieve and maintain reliable production-quality performance.

• Collaborate with data science and engineering teams to integrate AI models and agentic frameworks into existing workflows and platforms, ensuring seamless adoption and impact.

• Stay abreast of the latest advancements in AI, particularly in LLMs, agentic frameworks, and ML platforms, to identify new opportunities for innovation and process optimization.

📝 Enhancement Note: The responsibilities highlight a strong focus on the strategic application of AI within operational contexts, emphasizing use case identification, roadmap ownership, and cross-functional team leadership, all critical for driving operational efficiency and innovation.

🎓 Skills & Qualifications

Education: Postgraduate degree in a relevant field (e.g., Computer Science, Engineering, Data Science, Business, or a related technical discipline).

Experience: Significant product management or related technical experience (5-10 years) delivering AI products end-to-end, ideally within large, complex, matrixed organizations. Proven ability to ship AI-enabled products and lead complex programs from conception through to successful launch and iteration.

Required Skills:

• Product Management Expertise: Proven track record in end-to-end product lifecycle management, from ideation and strategy to execution and launch.

• AI & ML Familiarity: Deep understanding of AI model architectures, including Large Language Models (LLMs), and methods such as prompting, context engineering, fine-tuning, Retrieval Augmented Generation (RAG), model context protocols, and agentic frameworks.

• Technical Acumen: Ability to understand and articulate technical requirements, collaborate effectively with engineering teams, and guide technology teams through development cycles.

• Strategic Thinking: Strong capability in defining product strategy and developing comprehensive product roadmaps aligned with business objectives.

• Stakeholder Management: Excellent ability to manage relationships with senior stakeholders, drive alignment, and communicate complex technical concepts clearly to non-technical audiences.

• Structured Thinking: Demonstrated ability to break down complex problems, apply structured thinking, and connect product work directly to measurable business outcomes.

• Project Leadership: Experience leading cross-functional teams (engineers, designers, analysts, business leads) through technical and non-technical decisions to successful product launches.

• Process Optimization: Understanding of how to leverage AI and agentic services to automate and improve key operational workflows.

Preferred Skills:

• AI Evaluation & Iteration: Experience designing robust evaluation sets and conducting quantitative and qualitative iterations to achieve reliable production quality for AI models.

• Cloud & Data Platforms: Familiarity with cloud platforms (e.g., AWS) and data management/machine learning tooling.

• Financial Services Domain: Experience within the financial services industry, particularly in Corporate and Investment Banking operations, is a plus.

• Agile Methodologies: Proficiency in Agile development methodologies for iterative product development.

📝 Enhancement Note: The qualifications emphasize a blend of deep AI/ML technical knowledge, strategic product management skills, and strong leadership capabilities within a large organizational context, reflecting the demands of advanced AI product development in a regulated industry.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• AI Product Case Studies: Demonstrable examples of AI products or features successfully launched, detailing the problem statement, AI/ML approach, technical challenges, and quantifiable business impact.

• Roadmap & Strategy Examples: Samples of product strategy documents or roadmaps that illustrate how you translated business needs into a clear, actionable plan for AI product development.

• PRD/Requirements Samples: Examples of Product Requirements Documents (PRDs) or detailed user stories that showcase your ability to define clear, concise, and actionable requirements for technical teams.

• Stakeholder Communication: Evidence of effective communication strategies for engaging with and influencing senior stakeholders on AI product initiatives, potentially through presentation decks or executive summaries.

Process Documentation:

• Workflow Automation Design: Showcase how you've designed or documented workflows that incorporate AI or agentic services to improve efficiency, reduce manual effort, or enhance decision-making.

• AI Model Integration: Examples of how you've managed the integration of AI models (e.g., LLMs) into existing systems or processes, highlighting considerations for data flow, APIs, and user interfaces.

• Performance Measurement Frameworks: Documentation of how you've established metrics and frameworks to measure the performance and impact of AI products post-launch, including A/B testing methodologies or operational KPI tracking.

📝 Enhancement Note: For a role of this seniority and technical focus, a portfolio demonstrating not just product management but also a deep understanding of AI/ML product development lifecycle, strategic application of LLMs/agentic frameworks, and measurable operational impact is crucial.

💵 Compensation & Benefits

Salary Range: Based on industry research for Senior Product Manager roles with a specialization in AI and Machine Learning in London, the estimated salary range for this position is £90,000 - £130,000 per annum. This estimate accounts for the required experience level (5-10 years), the specialized technical skills (AI, LLMs, agentic frameworks), the seniority of the role, and the London location, which has a high cost of living and competitive tech talent market.

Benefits:

• Comprehensive health insurance coverage, including medical, dental, and vision plans.

• Generous paid time off (PTO), including vacation days, sick leave, and public holidays.

• Retirement savings plan (e.g., pension scheme) with employer matching contributions.

• Opportunities for professional development, including training, certifications, and conference attendance related to AI, ML, and product management.

• Employee assistance programs offering confidential support for personal and professional well-being.

• Access to company-wide employee resource groups (ERGs) fostering diversity and inclusion.

• Potential for performance-based bonuses and long-term incentives.

Working Hours: Standard full-time working hours are typically 40 hours per week. While the role is on-site, JPMorgan Chase & Co. often offers flexibility in scheduling, allowing for some adaptation to meet project deadlines and personal needs, with an expectation of availability during core business hours.

📝 Enhancement Note: The salary range is an estimation based on market data for similar roles in London. Actual compensation will depend on the candidate's specific experience, qualifications, and the company's internal compensation structure. The benefits listed are typical for large financial institutions in the UK.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services, specifically Corporate and Investment Banking. JPMorgan Chase & Co. operates at the forefront of global finance, providing a wide range of sophisticated financial products and services. This industry context implies a strong emphasis on security, compliance, risk management, and operational excellence.

Company Size: JPMorgan Chase & Co. is a very large, multinational corporation, employing over 290,000 people globally. This scale means significant resources, complex organizational structures, and opportunities to work on projects with substantial impact.

Founded: The company's origins trace back to 1799, with its current structure evolving through numerous mergers and acquisitions. This long history signifies stability, deep-rooted expertise, and a culture that balances tradition with innovation.

Team Structure:

• The AI Product Manager will likely be part of a dedicated AI or Digital Transformation team within the Corporate and Investment Banking (CIB) operations division.

• This team may consist of other product managers, AI/ML engineers, data scientists, business analysts, and project managers, reporting up through a Head of Product or Head of AI/Digital Innovation.

• Collaboration is expected to be highly cross-functional, involving partnerships with technology teams, business line leaders, compliance, legal, and other operational units across the bank. Methodology:

• Data-Driven Decision Making: A strong emphasis on using data analytics, performance metrics, and quantitative analysis to inform product strategy, roadmap prioritization, and feature development.

• Agile & Iterative Development: While operating within a large, regulated enterprise, Agile principles are likely applied to product development cycles, allowing for iterative progress, rapid prototyping, and continuous feedback loops.

• Risk Management & Compliance: All operational and product development activities will be conducted with a rigorous focus on risk mitigation, regulatory compliance, and data security, paramount in the financial services sector.

Company Website: https://www.jpmorganchase.com/

📝 Enhancement Note: The company's status as a global financial leader dictates a culture that values precision, security, and innovation. For an AI Product Manager, this means balancing cutting-edge technology development with stringent regulatory requirements and a need to demonstrate tangible, quantifiable business value.

📈 Career & Growth Analysis

Operations Career Level: This is a Senior Product Manager role, indicating a mid-to-senior level position. It requires significant experience (5-10 years) in product management, with a specialized focus on AI/ML. The role demands not just execution but also strategic leadership, influencing senior stakeholders, and owning product vision and roadmap for complex, cutting-edge technologies. It positions the individual as a key contributor to the bank's AI strategy and operational transformation.

Reporting Structure: The Senior AI Product Manager will likely report to a Director or VP level leader within the AI Product Management or Digital Operations division of the Corporate and Investment Bank. This leader will be responsible for overseeing the broader AI strategy and product portfolio. The role involves close collaboration and dotted-line reporting to various business and technology leaders across different departments.

Operations Impact: The primary impact of this role will be on enhancing operational efficiency, reducing costs, mitigating risks, and improving decision-making within the Corporate and Investment Bank through the strategic application of AI and agentic systems. Successful product launches will directly contribute to the bank's competitive advantage, revenue generation, and customer/client experience by automating complex processes and providing intelligent insights.

Growth Opportunities:

• Leadership Expansion: Potential to move into an Associate Director or Director of Product Management role, leading a larger team of product managers or owning a more extensive product portfolio.

• Specialization Deepening: Opportunity to become a subject matter expert in specific AI domains (e.g., Generative AI, Reinforcement Learning for Operations) or specialized areas within financial services operations.

• Strategic Influence: Growing influence on the bank's overall AI strategy, technology investments, and digital transformation initiatives.

• Cross-Departmental Mobility: Potential to transition into broader technology leadership roles, strategic initiatives, or even business line management roles leveraging deep operational and AI expertise.

📝 Enhancement Note: The seniority and specialization of this role offer significant career advancement potential within a leading financial institution, particularly in high-demand areas like AI and operational transformation.

🌐 Work Environment

Office Type: This role is designated as On-site, meaning the primary work environment will be within a JPMorgan Chase & Co. office location in London. This typically involves a professional corporate office setting designed for collaboration and productivity.

Office Location(s): The specific office is located at 25 Bank Street, Canary Wharf, London, E14 5JP. Canary Wharf is a major financial district, well-connected by public transport, offering a dynamic urban work environment with numerous amenities.

Workspace Context:

• Collaborative Spaces: Offices are equipped with meeting rooms, collaboration zones, and hot-desking areas to facilitate teamwork and spontaneous discussions.

• Technology Infrastructure: Access to robust IT infrastructure, high-speed internet, and the necessary hardware and software tools to perform the job effectively, including specialized AI development and collaboration platforms.

• Professional Atmosphere: A typically formal yet dynamic environment where interactions with colleagues, stakeholders, and leadership are frequent and professional, fostering a culture of accountability and excellence.

Work Schedule: The standard work schedule is full-time, typically 40 hours per week. While the role is on-site, there may be an expectation of flexibility to accommodate project deadlines, global team interactions (if applicable), and critical business needs, particularly in the fast-paced financial services sector.

📝 Enhancement Note: The on-site requirement in a prime London financial district suggests a structured, collaborative, and professional work environment, typical of large financial institutions where face-to-face interaction and secure, dedicated workspaces are prioritized.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will conduct an initial phone screen to assess basic qualifications, experience, and cultural fit.

• Technical/Product Interview(s): Expect in-depth interviews focusing on product management methodologies, AI/ML knowledge, strategic thinking, and experience with LLMs and agentic frameworks. These may involve case studies or hypothetical problem-solving scenarios.

• Stakeholder/Leadership Interview: Interviews with senior leaders or key stakeholders to evaluate strategic thinking, communication skills, and ability to influence and manage complex relationships.

• Team/Cultural Fit Interview: Discussions with potential team members to assess collaboration style and alignment with the team's and company's values.

• Final Round: A concluding interview, potentially with a very senior executive, to make a final decision.

Portfolio Review Tips:

• Quantify Impact: For each project in your portfolio, clearly articulate the business problem, the AI/ML solution implemented, your specific role, and most importantly, the quantifiable results (e.g., % increase in efficiency, % reduction in errors, $ saved).

• Showcase AI/LLM Depth: Highlight projects involving LLMs, agentic frameworks, RAG, fine-tuning, or prompt engineering. Explain the technical choices and challenges overcome.

• Structure for Clarity: Organize your portfolio logically, perhaps by product lifecycle stage or by type of AI application. Use clear headings and concise descriptions.

• Demonstrate Strategy: Include examples of how you developed product strategy, roadmaps, and PRDs, demonstrating your ability to translate business needs into actionable technology plans.

• Tailor to the Role: Emphasize experiences most relevant to financial services operations, AI product development, and stakeholder management in large organizations.

Challenge Preparation:

• AI Product Strategy Case Study: Be prepared to discuss how you would approach developing a product strategy for a new AI-driven operational tool within CIB. Consider identifying use cases, defining success metrics, and outlining a phased rollout.

• Technical Problem-Solving: Anticipate questions about specific AI/ML concepts (e.g., how RAG works, when to fine-tune vs. prompt, challenges of deploying LLMs in production).

• Stakeholder Alignment Scenario: Be ready to describe how you would gain buy-in from senior business leaders for a significant AI investment or a new product initiative that requires substantial change management.

📝 Enhancement Note: The interview process will likely be rigorous, testing both technical depth in AI product management and the ability to navigate a large, complex financial organization. A well-curated portfolio is essential for demonstrating practical experience and impact.

🛠 Tools & Technology Stack

Primary Tools:

• Product Management Platforms: Tools like Jira, Confluence, Aha!, Productboard for roadmap planning, backlog management, and documentation.

• AI/ML Platforms: Experience with cloud-based ML platforms (e.g., AWS SageMaker, Google AI Platform, Azure ML) for model development, deployment, and management.

• LLM Frameworks & Libraries: Familiarity with tools and libraries for working with LLMs, such as Hugging Face Transformers, LangChain, LlamaIndex, or similar frameworks for agentic orchestration.

• Collaboration Suites: Microsoft 365 (Teams, SharePoint, Outlook) or Google Workspace for daily communication, collaboration, and document sharing.

Analytics & Reporting:

• Business Intelligence Tools: Tableau, Power BI, or similar for creating dashboards and reports to track product performance and operational KPIs.

• Data Warehousing & Querying: Experience with SQL and potentially data warehousing concepts for data analysis and extracting insights from large datasets.

• Analytics Platforms: Familiarity with web analytics or product analytics tools to understand user behavior and product adoption.

CRM & Automation:

• CRM Systems: While not a direct CRM role, understanding how AI products integrate with or complement CRM functionalities (e.g., Salesforce) is beneficial.

• Workflow Automation Tools: Familiarity with tools or concepts related to business process automation (BPA) or robotic process automation (RPA) that AI solutions might augment or replace.

• Integration Tools: Understanding of APIs and integration patterns for connecting AI services with existing enterprise systems.

📝 Enhancement Note: Proficiency with a range of product management tools, cloud-based AI/ML platforms, and common enterprise collaboration suites is expected. Specific experience with LLM orchestration frameworks like LangChain or LlamaIndex would be highly advantageous.

👥 Team Culture & Values

Operations Values:

• Excellence & Precision: A core value in financial services, demanding accuracy, attention to detail, and high-quality output in all operational processes and AI product development.

• Innovation with Responsibility: A drive to adopt cutting-edge technologies like AI and LLMs, balanced with a strong commitment to ethical practices, risk management, security, and regulatory compliance.

• Collaboration & Teamwork: Emphasis on working effectively across diverse teams (tech, business, compliance) to achieve shared goals, fostering an environment where collective success is paramount.

• Data-Driven Insights: A culture that values and leverages data to inform decisions, measure performance, and drive continuous improvement in both operational processes and product development.

• Client Focus: Ultimately, all operational efforts and AI product innovations are geared towards serving clients effectively, whether internal business units or external customers, by improving service, efficiency, and value.

Collaboration Style:

• Cross-Functional Integration: The role requires seamless collaboration with engineering teams, data scientists, business analysts, and operational stakeholders. This involves active participation in planning, development, and review cycles.

• Transparent Communication: An open and transparent approach to sharing progress, challenges, and insights is valued, ensuring all stakeholders are informed and aligned.

• Feedback-Driven Improvement: A willingness to give and receive constructive feedback is essential for iterating on AI products and refining operational processes, fostering a culture of continuous learning.

• Problem-Solving Orientation: A proactive and collaborative approach to tackling complex challenges, encouraging diverse perspectives to find the most effective solutions.

📝 Enhancement Note: The culture at a major financial institution like JPMC emphasizes a blend of rigorous execution, strategic innovation, and strong ethical considerations. Candidates should be prepared to demonstrate how they embody these values in their work.

⚡ Challenges & Growth Opportunities

Challenges:

• Navigating Large Organizations: Successfully driving AI product initiatives within a large, complex, and highly regulated financial institution requires strong stakeholder management, political acumen, and the ability to influence without direct authority.

• Balancing Innovation with Risk: Implementing cutting-edge AI technologies like LLMs must be done while adhering to stringent security, compliance, and ethical guidelines inherent in the financial sector.

• Data Availability & Quality: Ensuring access to sufficient, high-quality data for training and deploying AI models can be a significant challenge within large enterprises, requiring robust data governance and collaboration.

• Measuring ROI of AI: Clearly demonstrating the tangible return on investment (ROI) for AI initiatives, especially those involving complex LLM applications, can be challenging and requires sophisticated measurement frameworks.

• Pace of AI Evolution: Keeping pace with the rapid advancements in AI and ML technologies and determining which innovations are most relevant and viable for enterprise adoption.

Learning & Development Opportunities:

• Advanced AI/ML Specialization: Access to internal training, external courses, and conferences focused on the latest developments in Generative AI, agentic systems, LLM fine-tuning, and responsible AI.

• Financial Services Domain Expertise: Deepening understanding of the intricacies of Corporate and Investment Banking operations, risk management, and regulatory landscapes.

• Leadership Development Programs: Opportunities to participate in leadership training, executive coaching, and mentorship programs designed to hone strategic thinking and management skills.

• Cross-Functional Exposure: Gaining exposure to various business units and technology functions within the bank, broadening understanding of the enterprise and potential career paths.

📝 Enhancement Note: The challenges highlight the unique environment of applying advanced AI in a regulated financial setting, while the growth opportunities point to significant potential for professional development and career advancement within the organization.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex AI product you launched end-to-end. What was the business problem, your strategy, the key technical challenges (especially with LLMs or agentic systems), and the measurable impact?"

• "How would you approach identifying and prioritizing AI use cases within Corporate and Investment Banking operations? What criteria would you use, and how would you validate their potential impact?"

• "Imagine you need to convince senior leadership to invest heavily in a new LLM-based operational automation tool. What is your strategy for building a business case and gaining buy-in?" Company & Culture Questions:

• "Why are you interested in applying AI and agentic technologies specifically within the financial services sector, and at JPMorgan Chase & Co.?"

• "How do you approach balancing innovation with the stringent regulatory and security requirements of a large financial institution?"

• "Describe a time you had to manage conflicting priorities or stakeholders with different objectives. How did you resolve it?" Portfolio Presentation Strategy:

• Start with Impact: Begin your portfolio walkthrough by highlighting your most impactful AI product success stories, focusing on the business value delivered.

• Deep Dive on AI/LLM: For relevant projects, be prepared to explain the AI/LLM architecture, the specific techniques used (e.g., RAG, fine-tuning, prompt engineering), and the rationale behind those choices.

• Showcase Process: Demonstrate your understanding of the full product lifecycle, from ideation and strategy to execution, launch, and iteration. Use PRDs or roadmap examples to illustrate this.

• Quantify Everything: Be ready to discuss metrics, KPIs, and ROI for all your projects. Use numbers and data to support your claims of success.

• Interactive Elements: If possible, prepare a brief demo or interactive walkthrough of a relevant product or prototype, or be ready to walk through mockups or diagrams that clearly explain functionality and user flows.

📝 Enhancement Note: Preparation should focus on articulating strategic thinking, deep AI/LLM technical understanding, and a proven ability to deliver tangible business results within a corporate environment.

📌 Application Steps

To apply for this operations-focused AI Product Manager position:

• Submit your application through the provided Oracle Cloud portal link, ensuring all sections are completed accurately and thoroughly.

• Curate Your Portfolio: Select 2-3 of your most relevant AI product management case studies, emphasizing projects involving LLMs, agentic systems, or significant operational automation. Quantify the impact with specific metrics and be ready to discuss the technical details and strategic decisions made.

• Optimize Your Resume: Tailor your resume to highlight keywords from the job description, such as "AI Product Management," "LLMs," "Agentic Frameworks," "Product Strategy," "Roadmap Development," "Stakeholder Management," and "Workflow Automation." Clearly list your experience with end-to-end product delivery and AI model architectures.

• Prepare Your Narrative: Practice articulating your experience and strategy using the STAR method (Situation, Task, Action, Result) for behavioral questions, and be ready to present your portfolio confidently, focusing on your contributions and the impact of your work.

• Research JPMorgan Chase & Co.: Understand the company's strategic initiatives in AI and digital transformation, particularly within their Corporate and Investment Bank operations, to demonstrate your alignment with their goals and culture.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.