Senior AI Product Leader — Enterprise Platforms

Edward Jones

Tempe (AZ)

On-site

USD 140,000 - 210,000

Full time

2 days ago

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Job summary

Edward Jones in Tempe, AZ is seeking a Senior AI Product Manager to shape and lead the firm's enterprise AI products, owning strategy, discovery, prioritization, adoption, and lifecycle management. You will partner across Engineering, Data, AI, Architecture, Experience, Risk, and Business teams to deliver measurable value while ensuring reliability, governance, and responsible AI practices.

Applicants should have 5+ years in Product Management and at least 1 year in AI-related roles; commuting

Qualifications

Bachelor's degree in a relevant technical, product, analytics, or business field.
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.

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.