Interested in this AI Product Manager role at EPAM Systems?
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We are growing a product-management team that runs AI-powered products and applies AI across the whole product lifecycle. As a Senior Product Manager | Applied AI, you will own the vision, strategy, roadmap and delivery of one or more products — at least one of them AI-powered — while using AI day-to-day to work faster and make sharper decisions and mentoring other Product Managers as you do.
What "Applied AI" means here. This is a product role, not an engineering role. We expect confident, practical command of AI as a product manager — using it across the lifecycle and shaping AI-powered features — not the ability to build, train or tune machine-learning models as an ML engineer or data scientist would.
Req# 1100335863
Responsibilities
- Own the vision, strategy, roadmap and end-to-end delivery of one or more products, including at least one that is AI-powered
- Define requirements and use cases for AI features, treating them as probabilistic products — scoping data and use-case fit, and setting evaluation criteria, quality bars, guardrails and human-in-the-loop review as acceptance criteria
- Identify where AI can improve the product, the customer experience, internal processes or business outcomes — and build and size the case for it
- Use AI tools productively and with discipline across discovery, research synthesis, analysis, documentation, user stories, prototyping and planning — verifying outputs before relying on them
- Prioritise features, own the backlog and manage release cycles, using AI-assisted analysis to support (not replace) your decisions
- Manage the full product lifecycle, including the added considerations of AI features — model/version changes, quality drift and human oversight
- Work closely with engineering, data, design and AI/ML teams, translating product intent into requirements they can act on and their constraints into product decisions
- Set realistic expectations with clients and stakeholders about what AI can and cannot do, and communicate benefits, limitations and risks in plain language
- Mentor Product Managers and raise the team's practical AI capability by example
Requirements
- 5+ years in Product Management, having managed one or more products end-to-end, including post-launch maintenance and support
- Launched more than one product (or key capability) to market, with at least one experience delivering or materially improving an AI-powered product or feature (e.g., GenAI, ML, recommendations, search or automation)
- Solid ownership of product strategy, vision and roadmap; market analysis and product visioning — including identifying and justifying where AI adds value
- Experience owning backlogs, cross-product dependencies and release cycles, and managing product lifecycle and support models
- Familiarity with product profitability, competitive positioning and pricing — including the cost, latency and quality trade-offs that shape AI-feature economics
- Expertise across multiple (3+) business domains, able to act as a business-domain SME
- Working AI literacy — what current AI (including GenAI/LLMs) can and cannot do reliably, common patterns and typical failure modes — enough to make sound product decisions and hold credible conversations with technical teams. Deep model-building knowledge is not required
- Able to influence stakeholders up to and including VP level, and to lead all critical aspects of a product launch
- Able to lead a team of Product Owners and/or Product Managers across a product line or family, guiding them through the full lifecycle
- Raises the team's practical AI proficiency — sharing verified ways of working and coaching on requirements and evaluation criteria for AI features
- Practical, daily use of AI across research, discovery, analysis, documentation, user stories, prototyping and planning — with verification of outputs
- Ability to identify and justify AI opportunities in products, processes, CX and business outcomes
- Working AI literacy — concepts, terminology, capabilities and limitations at PM depth
- Experience defining requirements and use cases for AI features, including evaluation criteria, quality bars, guardrails and human oversight
- Ability to evaluate AI outputs for quality, accuracy, risk, limitations and user impact
- Responsible-AI awareness — privacy, bias, security, transparency and human oversight
- Ability to work effectively with engineering, data, design and AI/ML teams
- Adaptability and continuous learning as AI tools and capabilities evolve
Nice to have
- Hands-on prototyping with AI builder tools to near-production fidelity
- Building PM agents / multi-step automations across research, backlog, analytics and comms
- Understanding of AI-feature economics (cost, latency, unit economics) and model lifecycle (drift, versioning, vendor change)
- Familiarity with AI regulation (e.g., EU AI Act) and relevant sector rules
- Prior experience taking an AI-powered product to production at scale
We offer
- Medical, Dental and Vision Insurance (Subsidized)
- Health Savings Account
- Flexible Spending Accounts (Healthcare, Dependent Care, Commuter)
- Short-Term and Long-Term Disability (Company Provided)
- Life and AD&D Insurance (Company Provided)
- Employee Assistance Program
- Unlimited access to LinkedIn learning solutions
- Matched 401(k) Retirement Savings Plan
- Paid Time Off – the employee will be eligible to accrue 15-25 paid days, depending on specific level and tenure with EPAM (accrual eligibility may change over time)
- Paid Holidays - nine (9) total per year
- Legal Plan and Identity Theft Protection
- Accident Insurance
- Employee Discounts
- Pet Insurance
- Employee Stock Purchase Program
- If otherwise eligible, participation in the discretionary annual bonus program
- If otherwise eligible and hired into a qualifying level, participation in the discretionary Long-Term Incentive (LTI) Program
This Remote Position Cannot be Performed in New York City.
EPAM is a leading global provider of digital platform engineering and development services. We are committed to having a positive impact on our clients, our employees, and our communities. We embrace a dynamic and inclusive culture. Here you will collaborate with multi-national teams, contribute to a myriad of innovative projects that deliver the most creative and cutting-edge solutions, and have an opportunity to continuously learn and grow. No matter where you are located, you will join a dedicated, creative, and diverse community that will help you discover your fullest potential.
Engineer the Future with a Career at EPAM
This posting includes a good faith range of the salary EPAM would reasonably expect to pay the selected candidate. The range provided reflects base salary only. Individual compensation offers within the range are based on a variety of factors, including, but not limited to: geographic location, experience, credentials, education, training, the demand for the role; and overall business and labor market considerations. Most candidates are hired at a salary within the range disclosed. Salary range: $150,000-$220,000 USD. This position is also eligible for variable compensation incentives. In addition, the details highlighted in this job posting above are a general description of all other expected benefits and compensation for the position.
Applications will be accepted on a rolling basis.
In accordance with the LA County Fair Chance Ordinance, you may find a copy of the Notice containing a summary of the Ordinance’s key provisions here: Concept FCO Posting 8 27 24 (lacounty.gov)
EPAM will not provide new H-1B visa sponsorship for this position. Candidates with existing transferable H-1B status may be considered.
It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.
Salary Context
This $150K-$220K range is below the median for AI Product Manager roles in our dataset (median: $193K across 99 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 9,653 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At EPAM Systems, 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 $220,200 based on 862 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($185K) sits 16% below the category median. Disclosed range: $150K to $220K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
EPAM Systems AI Hiring
EPAM Systems has 14 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Consultant, AI Software Engineer. Positions span Remote, US, Houston, TX, US, New York, NY, US. Compensation range: $90K - $260K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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