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About This Role
An Applied AI Product Manager is a senior individual contributor responsible for ensuring a product's value and viability within a product line. This role involves leading empowered, cross\-functional product teams to solve moderate complexity customer problems that align with high value business needs. The Applied AI Product Manager is accountable for the product's success, from vision to execution, and collaborates closely with various functions and stakeholders to deliver valuable, viable, usable, and feasible solutions. The
Applied AI Product Manager harnesses AI and agentic tools to compress the concept\-to\-cash learning loop\-automating analysis, prototyping, and compliance detail\-work so the team can focus on the human judgment AI cannot replace: product sense\-making.
The Applied AI Product Manager plays a crucial role in ensuring the success of our high value, moderately complex products by balancing customer needs with business objectives. This role requires a blend of strategic vision, analytical skills, and collaborative teamwork to deliver valuable, viable, usable, and feasible solutions. It demands significant experience in the modern product management craft and a drive for continuous improvement\-amplified by the fluent, responsible use of AI to learn faster and earn faster.
Recruiting for this role ends on 9/30/2026\.
Work you'll do
- Product Accountability
- + Responsible and accountable for the product's value and viability showcasing a measurable Return on Investments (ROI)
+ Drive strategy\-aligned solutions to achieve product value objectives.
+ Formulate and achieve Key Performance Indicators (KPIs) for identified problems to solve.
+ Measure KPIs and analyze outcomes to inform future strategies.
+ Leverage AI to harvest outcome evidence early and often, lowering total cost of ownership (TCO).
- Vision and Strategy
- + Co\-create, own, and evangelize the product vision, strategy, and roadmap, using AI to deepen domain knowledge and simulate future scenarios to chart pathways others have not yet seen.
+ Align product objectives with the product line and business goals.
+ Co\-create in collaboration with business stakeholders, engineering, experience, and delivery.
+ Use AI to expedite research, gather evidence, bolster domain knowledge, and craft and innovative visions backed by compelling strategic rationale.
- Market and User Engagement
- + Conduct user research and competitive analysis, using AI agents to synthesize research at speed\-accelerating the data crunching, ensuring the human connection.
+ Engage the team with users and stakeholders through continuous research and direct interactions.
+ Collaborate and guide the team toward solutions that address priority user and business needs.
+ Apply analytical skills to analyze data and derive actionable insights, shifting from waiting on analysis to working on insights.
+ Adopt innovative and experimental approaches to solving complex problems, including AI\-built, disposable prototypes that validate solutions quickly and retire bad ideas just as fast.
- Collaboration and Teamwork
- + Work side\-by\-side with cross\-functional (business, engineering, experience, and delivery) team members to achieve KPI outcomes.
+ Promote a product operating model that emphasizes outcomes over output (minimize overproduction while maximizing value).
+ Build empowered teams and product communities who exhibit collective product ownership and level\-up their outcome potential through AI and agentic tools
- Continuous Improvement
- + Promote and drive rapid, emergent, and ongoing learning and adaptation to meet objectives.
+ Drive innovation and improvement of the process to drive out waste and accelerate value achievement, using AI as a force\-multiplier to offload the repetitive, speed up the sluggish, and automate the mundane.
+ Remove obstacles for the team and ensure smooth flow of continuous value achievement.
+ Spread knowledge and best practices within the product vertical community.
- Applied AI Ways of Working
- + Amplify innovation: use AI to rapidly deepen domain knowledge, surface untapped market and user potential, and simulate future scenarios\-charting new pathways for the business.
+ Amplify learning: use AI agents to synthesize research and validate ideas before they enter the backlog\-compressing lead time by accelerating the data crunching, ensuring the human connection.
+ Amplify focus: act as Editor\-in\-Chief\-using AI to rigorously test assumptions and retire ideas that do not genuinely serve the user's workflow in a way that works for the business.
+ Amplify experimentation: use AI to build early, functional, disposable prototypes that validate the architecture and the solution, playing a key role in the Agentic Secure Software Development Life Cycle that paves a clear path to productionize early and often.
The successful candidate will possess:
- Ability to work independently and collaborate as part of a team
- Effective written and verbal communication skills
- Meticulous attention to detail and quality of work product
- Ability to build and sustain professional relationships
- Ability to lead projects or workstreams
- Ability to manage and prioritize multiple tasks in a fast\-paced and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to mentor and provide clear guidance to others
The teamDeloitte Product Engineering (PxE) is developing advanced, agentic AI\-enabled solutions that are redefining the future of work across our organization and for global clients. We are committed to bringing together outstanding product, engineering, and design talent to lead this transformation.
Qualifications
Required:
- Bachelor's degree in business, Marketing, Engineering, or a related field.
- 6\+ years of proven experience in lean product management or related roles.
- 3\+ years enterprise scale experience across multiple business areas.
- 1\+ years of building AI based intelligent products
- 1\+ year's experience in using GenAI tools to perform product management tasks like conduct idea research, shaping, synthesis, roadmaps, requirements, prototyping, testing
- Limited immigration sponsorship may be available
- Ability to travel 0\-20%, on average, based on the work you do and the clients and industries/sectors you serve
- Ability to work in your local office at a minimum of 3 days per week
- Candidates must be located within a commutable distance to one of the select location available for this role
Preferred:
- MBA or related advanced degree
- Demonstrated experience in modern product craft of delivering the right thing, in the right way, at the right time. Significant experience in lean product management craft and domain (tools, methods, and practices). Seen as a leader in this space.
- Proven accountability for value, viability and P\&L objectives for a product and for an empowered product team.
- Customer\-Centricity: Deep understanding of customer needs and engagement patterns, driving teams to deliver solutions that customers love and that work for the business. Expertise in applying customer\-centric methods and practices.
- AI Agentic Fluency: Comfortable orchestrating multiple AI agents across the concept\-to\-cash flow (research, insight, prototyping, specification, coding, and compliance), with guardrails at each hand\-off\-assumptions, confidence levels, and links to sources of truth.
- AI Realism and Eval Fluency: Understands the difference between deterministic logic and probabilistic generation; designs guardrails for hallucination, bias, and drift; uses evaluation harnesses before launch and monitors drift after, with a kill\-switch mentality\-and knows when not to use AI.
- Experience with modern agentic AI tools such as Claude Code, Claude Co\-work, Open AI Codex Cursor, and Visual Studio Code.
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $113,100 to $232,300\.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
Salary Context
This $113K-$232K range is below the median for AI Product Manager roles in our dataset (median: $185K across 167 roles with salary data).
View full AI Product Manager salary data →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 Deloitte, 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 Required
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. This role's midpoint ($172K) sits 20% below the category median. Disclosed range: $113K to $232K.
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.
Deloitte AI Hiring
Deloitte has 59 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Engineer, Research Engineer. Positions span Rosslyn, VA, US, Baltimore, MD, US, Morristown, NJ, US. Compensation range: $140K - $379K.
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.
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