Senior AI Product Manager

Edward Jones

23h•Hybrid

About The Position

The Senior AI Product Manager shapes and leads the firm's most complex and strategically significant enterprise AI products and reusable AI capabilities serving multiple products, business domains, customer experiences, and enterprise workflows. This role treats AI as the product and owns strategy, discovery, prioritization, adoption, lifecycle management, and measurable outcomes. Operating with significant autonomy, the Senior Lead sets product direction and ensures scalable AI solutions create measurable value. This role may be accountable for one or more strategic AI product or capability domains. Based on organizational priorities, customer needs, and business strategy, these domains may enable AI creation and adoption; deliver AI-powered experiences; provide trusted information and intelligence; drive automation and decision support; or ensure governance, reliability, and operations. Products and services may evolve over time and can include models, agents, skills, functions, tools, knowledge assets, retrieval capabilities, builder experiences, low-code solutions, platform capabilities, governance services, observability capabilities, and other emerging AI technologies. The role influences strategy across interconnected AI products, helps shape enterprise AI direction, and advises business, technology, product, risk, and operational leaders. You will partner across Engineering, Data, Product Management, Architecture, Experience, Risk, and Business areas to deliver customer and business value while ensuring capabilities are scalable, reusable, explainable, measurable, and aligned with responsible AI practices. The Senior AI Product Manager also strengthens AI Product Management across the organization through product leadership, coaching, mentoring, collaboration, and advancement of best practices.

Requirements

Bachelor's degree in a relevant technical, product, analytics, or business field, or equivalent professional experience.
Five or more years of Product Management experience, including ownership of complex technology, platform, data, digital, or AI products.
1+ 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.
Familiarity in at least one AI product specialty, with broad knowledge of enterprise AI technologies, data, platforms, governance, and architecture.
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.
A strong understanding of responsible AI, governance, privacy, security, compliance, model risk, monitoring, and enterprise controls.
Proven ability to influence 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

Set the vision, value proposition, outcomes, priorities, and roadmap for strategic AI products and capability domains.
Translate customer needs and business priorities into scalable AI product strategies, investment recommendations, product outcomes, requirements, evaluation criteria, and prioritized work.
Identify opportunities, dependencies, risks, overlaps, and tradeoffs across interconnected AI products and capabilities.
Own strategic AI products across the lifecycle, from discovery and launch through adoption, optimization, and retirement.
Define customers, use cases, value propositions, success measures, constraints, service expectations, and lifecycle plans.
Lead continuous discovery to identify customer problems, unmet needs, reusable patterns, and high-value AI opportunities.
Test product hypotheses through research, prototypes, experiments, evaluations, proofs of concept, and pilots. Use customer evidence, product data, technical findings, and business measures to recommend whether to pursue, change, scale, pause, or stop an opportunity.
Define the product experience, including AI behavior, data and knowledge needs, integrations, human oversight, controls, monitoring, and support.
Partner with Engineering, Data, AI Science, Architecture, Cybersecurity, Experience, and Business teams to evaluate options, challenge assumptions, identify dependencies, and make informed tradeoffs.
Promote reusable, interoperable patterns across models, agents, skills, knowledge assets, workflows, platforms, and governance capabilities.
Recognize and address AI limitations and risks, including unreliable outputs, bias, data leakage, inappropriate actions, security threats, model drift, and workflow failure.
Define and monitor measures for customer value, business outcomes, adoption, reuse, quality, reliability, cost, operational performance, scalability, and risk.
Embed responsible AI, privacy, security, legal, regulatory, accessibility, records, and model-risk requirements throughout the product lifecycle. Define intended uses, limitations, potential misuse or harm, data sensitivity, autonomy, access, human oversight, controls, and escalation paths.
Communicate product outcomes, health, risks, assumptions, dependencies, tradeoffs, and lessons learned to senior technical and nontechnical leaders and stakeholders; serve as a trusted advisor on complex AI opportunities and decisions.