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Description
Title: Product Manager, Agentic AI Platform
Location: Charlotte, NC
Duration: 6 months
Work Engagement: W2
Work Schedule: Hybrid 3 days in office/2 days remote
Benefits on offer for this contract position: Health Insurance, Life insurance, 401K and Voluntary Benefits
Summary:
In this contingent resource assignment, you may: Consult as an expert to develop or influence initiatives and resources for highly complex business and technical needs across Product Management. Consult on the strategy and resolution of highly complex and unique challenges requiring in\-depth evaluation across multiple areas, delivering solutions that are long\-term, large\-scale and require vision, creativity, innovation, and advanced analytical and inductive thinking. Provide expertise to client senior leadership on innovative Product Management business solutions. Strategically engage with client personnel. Required Qualifications: Product Management experience, or equivalent demonstrated through one or a combination of the following: work or consulting experience, training, military experience, education.
Key Responsibilities:
*Enterprise Capability Ownership*
Define and scale the enterprise Observe capability and its operating model, ensuring consistent, secure deployment with measurable business value across the company.
Build and expand the Agentic capability that converts raw video captures into executable agentic workflows/code, driving automation acceleration across operations and enterprise functions.
Own the end‑to‑end strategy for Observe, spanning video capture, telemetry ingestion, persona configuration, clickstream processing, and data collection across diverse business domains.
*Product Vision, Roadmap \& Multi‑Release Strategy*
Own the product vision, roadmap, and multi‑release plan for the Video‑to‑Code pipeline and broader agent creation ecosystem.
Leverage multimodal ingestion, semantic understanding, and hybrid deterministic \+ LLM architectures aligned with the O2A roadmap.
Establish scalable enterprise patterns for video‑to‑API code generation, enabling transformational delivery velocity (e.g., automating 30\+ control reviews per quarter).
*Integration Across the O2A Value Stream*
Partner with COO Technology and SkanAI to define the seamless transition from Observe Analyze Optimize Run, shaping the end‑to‑end value stream from data capture to agent execution.
Define compliance, control, and model assurance standards—including PII redaction, safe‑use patterns, and governance workflows.
*Enterprise Process Intelligence \& Automation Insight Frameworks*
Develop enterprise guidelines for process mapping, mining, behavioral clustering, and insight generation to identify high‑value automation opportunities.
Shape and operate O2A experimentation frameworks in collaboration with CIO leadership and COO Tech (e.g., Cloud PC environments, Digital Identity, containerized deployments).
*Advanced AI Capability Strategy*
Own the strategy for integrating open‑source multimodal and agentic models (e.g., Gamma‑3, large action models) into the agent creation pipeline.
Define requirements for foundational platform services such as structured insights extraction, ontology and knowledge‑graph generation, clickstream mapping, and automated workflow generation.
*Cross‑Functional Leadership \& Delivery Management*
Lead cross‑functional delivery teams—including Data Science, AI Engineering, Skan Configurators, and Platform Engineering—to deliver capabilities such as:
*Multimodal ingestion*
SOP generation
Semantic graph construction
Workflow translation
Agent execution validation
Multi‑agent orchestration
Define quality, governance, and assurance requirements including hallucination controls, SLA breach alerts, execution rollback mechanisms, analyst‑in‑the‑loop checkpoints, and observability (ARIZE, Phoenix, Splunk).
*Enterprise Strategy, Governance \& Change Leadership*
Establish enterprise release and change‑management routines for O2A, ensuring LOB alignment, training, onboarding, and readiness.
Collaborate with SMEs across operations, fraud, complaints, IT Ops, and contact centers to translate manual workflows into reusable agentic patterns.
Deliver long‑term, large‑scale capabilities that require creativity, innovation, and advanced analytical reasoning.
Provide strategic vision and expert guidance to senior leadership on the implementation of meaningful digital transformation initiatives.
Engage with and influence stakeholders at all levels across the enterprise, serving as a trusted advisor and thought leader.
Key Requirements:
- Applicants must be authorized to work for ANY employer in the U.S. This position is not eligible for visa sponsorship.
- Digital product management experience, or equivalent through professional experience, training, military background, or education.
- Technical expertise in multimodal machine learning, generative AI workflows, agent architectures, and automation design.
- Demonstrated ability to define and scale enterprise‑grade AI or automation capabilities.
- Strong systems‑thinking mindset with the ability to design operationally scalable and compliant AI systems.
- Proven experience influencing senior leadership and technical teams, shaping strategy and prioritization.
- Track record of delivering complex AI‑enabled services across multiple engineering organizations.
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Wells Fargo, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $217,100 based on 471 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Wells Fargo AI Hiring
Wells Fargo has 19 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Software Engineer, AI Product Manager. Positions span Charlotte, NC, US, Woodbridge, NJ, US, Concord, CA, US. Compensation range: $224K - $355K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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