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About This Role
As Manager of Product Development, AI Platform at Epicor, you will be leading the development of a next\-generation artificial intelligence agent platform while building and managing a high\-impact Forward Deployed Engineering (FDE) team. You will drive the adoption of artificial intelligence across the organization, embedding intelligent development practices into how software is built, delivered, and scaled. You will play a key role in shaping enterprise workflows and business automation through cutting\-edge technologies, while partnering with product, engineering leadership, and customers to deliver impactful solutions.
What you will be doing:* Leading, mentoring, and growing a global team of software engineers and Forward Deployed Engineers, fostering a high\-performance, artificial intelligence–first culture
- Driving adoption of artificial intelligence across the software development lifecycle to improve engineering productivity, quality, and velocity
- Managing team execution, delivery, hiring, performance management, and career development while ensuring operational excellence
- Partnering with Product Management and senior engineering leaders to define and deliver scalable artificial intelligence agent platforms and enterprise capabilities
- Designing and contributing to cloud\-native solutions leveraging Microsoft Azure, Python, and C\# technologies
- Building and leading a Forward Deployed Engineering function that collaborates directly with customers to validate use cases and accelerate artificial intelligence adoption
- Translating customer feedback and real\-world implementations into reusable platform features and product enhancements
What you will likely bring:* 8\+ years’ experience in software engineering with 3\+ years’ experience managing engineering teams
- Proven experience leading enterprise platform, cloud\-native, or artificial intelligence–focused development teams
- Strong expertise in Python and C\# development
- Deep experience designing and operating distributed cloud applications on Microsoft Azure
- Experience building or managing artificial intelligence, machine learning, generative AI, large language models, or workflow automation solutions
- Strong understanding of modern software architecture including APIs, microservices, event\-driven systems, and enterprise integration patterns
- Demonstrated success driving artificial intelligence adoption and development practices within engineering organizations
- Strong leadership, communication, and stakeholder management skills with the ability to influence across teams and customers
What could set you apart:* Experience building artificial intelligence agent frameworks, multi\-agent systems, or orchestration platforms
- Experience integrating artificial intelligence with enterprise applications such as ERP systems or complex business platforms
- Hands\-on experience with Azure AI Services, Azure OpenAI, Azure Kubernetes Service, Azure Functions, and Cosmos DB
- Knowledge of modern artificial intelligence frameworks including retrieval\-augmented generation, vector databases, orchestration frameworks, and observability tools
- Familiarity with emerging artificial intelligence ecosystems and agentic architectures
- Experience scaling engineering teams and delivering highly available enterprise platforms
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About Epicor
At Epicor, we’re truly a team. Join 5,000 talented professionals in creating a world of better business through data, AI, and cognitive ERP. We help businesses stay future\-ready by connecting people, processes, and technology. From software engineers who command the latest AI technology to business development reps who help us seize new opportunities, the work we do matters. Together, Epicor employees are creating a more resilient global supply chain.
We’re Proactive, Proud, Partners .
Whatever your career journey, we’ll help you find the right path. Through our training courses, mentorship, and continuous support, you’ll get everything you need to thrive. At Epicor, your success is our success. And that success really matters, because we’re the essential partners for the world’s most essential businesses—the hardworking companies who make, move, and sell the things the world needs.
Competitive Pay \& Benefits
- Health and Wellness: Comprehensive health and wellness benefits designed to support your overall well\-being.
- Internal Mobility: Opportunities for mentorship, continuing education, and focused career goal setting, with 25% of positions filled internally.
- Career Development: Free LinkedIn Learning licenses for everyone, along with our Mentoring Program to boost your personal development.
- Inclusive Workplace: Collaborate with a diverse team in an inclusive, global workplace that fosters innovation and celebrates partnership.
- Work\-Life Balance: Policies built on mutual trust and support, encouraging time off to rest, recharge, and reconnect.
- Global Mobility: Comprehensive support for international relocations and permanent residency processes.
Equal Opportunities and Accommodations Statement
Epicor is committed to creating a workplace and global community where inclusion is valued; where you bring the whole and real you— that’s who we’re interested in. If you have interest in this or any role\- but your experience doesn’t match every qualification of the job description, that’s okay\- consider applying regardless.
We are an equal\-opportunity employer.
Range:
Minimum: $138,000 USD Maximum: $235,000 USD
The salary range provided reflects the national average for this job title and does not represent compensation specific to Epicor Software Corporation. Actual compensation will vary based on experience, qualifications, and market factors relevant to the position.
Recruiter:
Matthew Brady
Salary Context
This $138K-$235K range is below the median for AI Product Manager roles in our dataset (median: $187K across 164 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 4,133 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Epicor Software, 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 $213,800 based on 610 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($186K) sits 13% below the category median. Disclosed range: $138K to $235K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Epicor Software AI Hiring
Epicor Software has 1 open AI role right now. They're hiring across AI Product Manager. Based in Herndon, VA, US. Compensation range: $235K - $235K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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