Lead AI Product Manager

Edward Jones

10d•Hybrid

About The Position

The Lead AI Product Manager is responsible for shaping and leading the firm's most complex and strategically significant enterprise AI products and reusable AI capabilities that serve multiple products, business domains, customer experiences, and enterprise workflows. This role treats AI as the product, owning the strategy, discovery, prioritization, adoption, lifecycle management, and measurable outcomes of shared AI capabilities rather than discrete applications. Operating with significant autonomy, the Senior Lead defines AI product direction, drives discovery and prioritization, and ensures measurable value creation through scalable, reusable AI solutions. The Lead AI Product Manager may be accountable for one or more strategic AI product or capability domains. Depending on organizational priorities, customer needs, and business strategy, these domains may focus on enabling AI creation and adoption, delivering AI-powered experiences, providing trusted information and intelligence, driving automation and decision support, or ensuring the governance, reliability, and operation of AI solutions. Specific products and services within these domains may evolve over time and can include reusable AI capabilities such as models, agents, skills, functions, tools, knowledge, retrieval capabilities, builder experiences, low-code solutions, platform capabilities, governance services, observability capabilities, and other emerging AI technologies. This role influences product and capability strategy across interconnected AI products, helps shape enterprise AI direction, and serves as a trusted advisor to business, technology, product, risk, and operational leaders. The Senior Lead works closely with colleagues across Engineering, Data, Product Management, Architecture, Experience, Risk, and Business areas to ensure AI capabilities deliver measurable customer and business value while remaining scalable, reusable, explainable, and aligned with responsible AI practices. The role also strengthens AI Product Management capabilities across the organization through product leadership, coaching, mentoring, collaboration, and the advancement of AI Product Management best practices. Specific products within these domains may include models, agents, skills, functions, tools, retrieval capabilities, low-code solutions, builder experiences, observability capabilities, governance services, and other emerging AI technologies. The Lead AI Product Manager is expected to provide strategic product leadership for a capability domain while ensuring the underlying products, services, and experiences are scalable, reusable, measurable, and aligned with enterprise objectives.

Requirements

BACHELOR'S DEGREE IN A RELEVANT TECHNICAL, PRODUCT, ANALYTICS, OR BUSINESS FIELD, OR EQUIVALENT PROFESSIONAL EXPERIENCE.
7+ years of Product Management experience, including ownership of complex technology, platform, data, digital, or AI products.
3+ years of experience with AI, machine learning, generative AI, agentic systems, intelligent automation, AI platforms, or advanced analytics.
Demonstrated success owning strategic products or reusable capabilities across multiple customers, teams, domains, products, or enterprise workflows.
Deep expertise in at least one AI product specialty, with broad knowledge of enterprise AI technologies, data, platforms, governance, and architecture.
Advanced Product Management skills in strategy, discovery, experimentation, prioritization, roadmaps, analytics, adoption, and lifecycle management.
Ability to translate complex customer and business needs into product outcomes, technical requirements, evaluation criteria, and prioritized work.
Experience defining product success measures and evaluating tradeoffs across customer value, feasibility, cost, quality, performance, reuse, risk, and time to value.
Advanced understanding of responsible AI, governance, privacy, security, compliance, model risk, monitoring, and enterprise controls.
Proven ability to influence senior leaders, lead cross-functional teams, coach others, and communicate complex topics clearly.

Nice To Haves

Master's degree in a relevant technical, product, or business field.
Experience leading enterprise AI products or reusable capabilities in financial services or another highly regulated industry.
Experience with low-code, no-code, and pro-code AI ecosystems, including Microsoft Copilot Studio, Power Platform, Azure AI, Databricks, or comparable technologies.
Experience defining AI evaluation, monitoring, observability, governance, security, privacy, model-risk, or regulatory controls.
Experience developing Product Management frameworks, organizational capabilities, communities of practice, or talent through coaching and mentoring.

Responsibilities

Define the vision, value proposition, outcomes, priorities, roadmap, and investment strategy for strategic AI products and capability domains.
Translate enterprise priorities and customer needs into scalable product strategies, requirements, evaluation criteria, and prioritized work.
Lead continuous discovery and test hypotheses through research, prototypes, experiments, evaluations, proofs of concept, and pilots.
Use customer evidence, product data, technical findings, and business measures to guide prioritization, investment, scaling, optimization, and retirement decisions.
Own the full product lifecycle from discovery and launch through adoption, operation, modernization, and retirement, remaining accountable for customer value, business outcomes, reuse, product health, risk, and value realization.
Define the end-to-end product experience, including customers, use cases, AI behavior, data and knowledge needs, integrations, human oversight, controls, monitoring, support, and service expectations.
Create reusable AI products with clear boundaries, consumers, interfaces, dependencies, supported uses, limitations, ownership, and operating expectations.
Promote interoperable patterns across models, agents, skills, knowledge assets, workflows, platforms, and governance capabilities while balancing local needs with enterprise scale and standardization.
Apply deep AI product expertise and technical fluency to challenge assumptions, evaluate alternatives, identify dependencies, and make informed tradeoffs with engineering, data, architecture, cybersecurity, experience, and business teams.
Recognize and manage AI limitations and risks, including unreliable outputs, bias, data leakage, inappropriate actions, security threats, model drift, and workflow failure.
Define success measures for customer value, business outcomes, adoption, reuse, quality, reliability, cost, operational performance, scalability, and risk.
Lead cross-functional teams toward measurable outcomes and communicate product health, risks, assumptions, dependencies, tradeoffs, and lessons learned to senior leaders and stakeholders.
Define launch, integration, enablement, documentation, change, support, monitoring, and adoption strategies, and resolve barriers to effective operation and scale.
Embed responsible AI, privacy, security, legal, regulatory, accessibility, records, and model-risk requirements throughout the product lifecycle, with clear intended uses, limitations, controls, oversight, and escalation paths.
Serve as a trusted advisor, coach and mentor Product Managers, and establish reusable frameworks, standards, evaluation practices, and customer-centered ways of working across the firm.