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About This Role
Description
About Alvarez \& Marsal
Alvarez \& Marsal (A\&M) is a global consulting firm with entrepreneurial, action and results\-oriented professionals. We take a hands\-on approach to solving our clients' problems and assisting them in reaching their potential. Our culture celebrates independent thinkers and doers who positively impact our clients and shape our industry. The collaborative environment and engaging work—guided by A\&M's core values of Integrity, Quality, Objectivity, Fun, Personal Reward, and Inclusive Diversity—are why our people love working at A\&M.
The Team
The Global AI \& Knowledge Organization (GAIKO) is A\&M's central engine for AI strategy, enablement, and knowledge infrastructure. Led by the Chief AI \& Knowledge Officer, the team bridges firm strategy and frontline delivery, responsible for how A\&M adopts, governs, and scales AI across its practices and geographies.
The team's mandate spans the full lifecycle of AI at A\&M: from rolling out enterprise tools like Claude to thousands of practitioners across the US, Australia, and Europe, to building the platforms and governance frameworks that ensure AI work is reusable, compliant, and compounding in value over time.
How you will contribute
The GAIKO team is building the A\&M AI Marketplace, a centralized platform that transforms the AI tools, prompts, agents, and other AI products created across our practices into reusable firm\-wide assets. The Product Manager will own the strategy, roadmap, and day\-to\-day execution of this platform, serving as the connective tissue between practitioners, technologists, governance stakeholders, and firm leadership.
The Marketplace exists to solve a real problem: high\-value AI work built across the firm too often stays siloed or disappears at project close. This platform changes that, making AI solutions broadly discoverable and turning the work of individuals and teams into a persistent, shared firm asset. By giving all practitioners access to proven, compliant assets, we raise A\&M’s baseline AI capability and reduce the risk of inconsistent or non\-compliant work in the field.
This is a high\-visibility, high\-impact role operating at the intersection of AI enablement and governance, knowledge management, and product development. The incoming PM will onboard into a live platform with an established governance framework, contributor network, and stakeholder ecosystem. The right candidate will bring both product discipline and a genuine enthusiasm for how AI is reshaping professional services. You will have direct influence over how thousands of A\&M practitioners discover and deploy AI, and how the firm captures and compounds its institutional knowledge over time.
- Own and evolve the Marketplace product roadmap, driving the platform from initial MVP through scaled self\-service contribution and automated repository ingestion
- Define and track success metrics including monthly active users, asset reuse rates, time\-to\-publish, SLA compliance, and approval rates
- Manage the end\-to\-end asset review process, collaborating with asset curators, BU/Topic Leaders and representatives, and offshore support to ensure security and risk compliance, quality governance, and timely curation of contributed assets
- Develop and manage the asset maintenance process, including asset deprecation and archival policy, to ensure AI Marketplace asset library is relevant, non\-duplicative, and continuously improving.
- Manage, maintain, and evolve role\-based permissions, firm\-aligned taxonomy, and metadata standards that enable reliable search and discovery
- Develop and manage an incentive or contributor recognition model for asset submission
- Drive user adoption across practices and geographies through active engagement with BU representatives, communication, training, and a practitioner feedback loop
- Partner with UX design and development team to prioritize the platform backlog and translate practitioner needs into product requirements
- Engage the Operating Committee and firm leadership with clear, data\-driven updates on platform performance and strategic direction
- Serve as the firm’s internal subject matter expert on the Marketplace, evangelizing the value of shared AI assets and reuse over bespoke builds
Qualifications
Required
- 8\+ years of product management experience, ideally in enterprise SaaS, knowledge management, or AI/ML tooling
- Demonstrated ability to define a product vision, build a roadmap, and drive results against clear success criteria
- Comfort operating and influencing in a matrixed professional services environment with multiple senior stakeholders
- Familiarity with AI concepts including hallucination and output reliability, LLM\-based tooling, Retrieval\-Augmented Generation (RAG), and Model Context Protocol (MCP) standards, or the appetite to learn quickly
- Strong communication and stakeholder management skills; ability to translate technical concepts for business audiences and vice versa
- Data\-oriented: comfortable defining KPIs, interpreting usage analytics, and using evidence to prioritize
Preferred
- Experience with governed content platforms, asset repositories, or internal marketplace concepts is strongly preferred
- Familiarity with enterprise workflow/intake tools (such as ServiceNow) is preferred
- Prior consulting or professional services experience is a plus
Your journey at A\&M
We recognize that our people are the driving force behind our success, which is why we prioritize an employee experience that fosters each person’s unique professional and personal development. Our robust performance development process promotes continuous learning, rewards your contributions, and fosters a culture of meritocracy. With top\-notch training and on\-the\-job learning opportunities, you can acquire new skills and advance your career.
We prioritize your well\-being, providing benefits and resources to support you on your personal journey. Our people consistently highlight the growth opportunities, our unique, entrepreneurial culture, and the fun we have together as their favorite aspects of working at A\&M. The possibilities are endless for high\-performing and passionate professionals.
Regular employees working 30 or more hours per week are also entitled to participate in Alvarez \& Marsal Holdings’ fringe benefits consisting of healthcare plans, flexible spending and savings accounts, life, AD\&D, and disability coverages at rates determined periodically as well as a 401(k) retirement savings plan. Provided the eligibility requirements are met, employees will also receive an annual discretionary contribution to their 401(k) retirement savings plan from Alvarez \& Marsal. Additionally, employees are eligible for paid time off including vacation, personal days, seventy\-two (72\) hours of sick time (prorated for part time employees), ten federal holidays, one floating holiday, and parental leave. The amount of vacation and personal days available varies based on tenure and role type. Click here for more information regarding A\&M’s benefits programs.
The salary range is $160,000 \- $190,000 annually, dependent on several variables including but not limited to education, experience, skills, and geography. In addition, A\&M offers a discretionary bonus program which is based on a number of factors, including individual and firm performance. Please ask your recruiter for details.
Salary Context
This $160K-$190K range is below the median for AI Product Manager roles in our dataset (median: $188K across 140 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 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Alvarez & Marsal, 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 $216,175 based on 270 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($175K) sits 19% below the category median. Disclosed range: $160K to $190K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Alvarez & Marsal AI Hiring
Alvarez & Marsal has 1 open AI role right now. They're hiring across AI Product Manager. Based in New York, NY, US. Compensation range: $190K - $190K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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