Dir, Product Management

Overview

Help build the world's first Cognitive ERP.

This leader owns the product strategy and execution for Prism, Epicor's developer platform and customer-facing agent framework over our ERPs. The job has two halves: build Prism's core agents and architecture, and scale Prism into a platform that other teams across Epicor, and our customers and partners, build on.

This is primarily a 1-to-100 role, not a 0-to-1 one. There are real 0-to-1 elements (new core agents, new framework surfaces, new architecture), but the center of gravity is taking what works and scaling it, turning Prism into a platform-as-a-product with the developer experience, golden paths, governance, and reuse that let many teams ship agents on it instead of each rebuilding their own.

AI is built into the work itself: into how Prism is built, how teams build on it, how it is governed, and how it earns revenue. You set the vision and roadmap for Prism's agents, agent framework, retrieval, ontology, extensibility, and AI UX, while operating in the AI-native way of working our product organization is moving to.

Epicor

Epicor is a technology leader in ERP for manufacturing, distribution, and retail, with deep roots in both enterprise software and startup culture. Our product organization is in the middle of a deliberate move to a platform-first, AI-enabled delivery model: converging products to destination platforms, broadening team roles, and treating shared capabilities as products that teams consume rather than rebuild. Prism is one of those shared capabilities, and this role is at the front of making it one teams want to build on.

How This Team Works (the Agentic Operating Model)

The biggest change versus a traditional PM job is how the work gets done. We expect this leader to operate fluently in the model below and to help the rest of the org adopt it.

• Platform as a product. Prism is built for the people who build on it. Adoption, reuse, developer experience, and golden paths are first-class outcomes, not afterthoughts to the core agents. Success is measured by how much other teams ship on Prism instead of rebuilding.
• Frontier teams, not feature factories. Delivery happens in small, human-led, agent-augmented teams (typically a few strong engineers/designers plus a fleet of specialist agents) operating against a human-governed control plane. The PM is inside that loop, not adjacent to it.
• Role convergence. In the agentic era, the lines between Product, Development, UX, and Data Science blur. The PM is a decision-maker who works directly in prototypes, specs, and evals, not a backlog groomer or status tracker handing requirements over a wall. You keep a clear primary discipline (product) and clear decision rights while absorbing adjacent work that AI now makes tractable.
• Spec, eval, control plane. The unit of work is a clear spec, the evals that prove it, and supervised agent execution against governed guardrails. "Done" is defined by evidence (eval pass rates, traces, and outcome data) captured continuously, not validated at the end. The framework you ship gives the teams building on Prism the same discipline.
• Reuse before rebuilding. Prism is the platform that makes reuse possible: a shared agent catalog, approved patterns, and eval templates. You partner with the AI Enablement function to prevent duplicate agents and fragmented standards across Epicor, and you keep the reusable surface coherent.
• Responsibility as the moat. AI will commoditize interaction with the ERP faster than it commoditizes responsibility for outcomes. Governance, identity, auditability, controls, and reversibility are first-class surfaces, both for Prism's own agents and for everything built on it.
• Learn by sharing. Wins, failures, prompts, and evals are shared openly so fluency compounds across the org. AI is part of how we work every day, not a side initiative.

What We'd Love to See

• Working Backwards for Two Customers: Mastery of customer empathy, working backwards from real painful workflows for end customers, and from real friction for the developers and teams who build on Prism. You validate the problem, the ideal profile, and the value proposition for both before committing build.
• Platform-as-a-Product Instinct: You think about reuse, golden paths, self-service, extensibility, and adoption the way a great consumer PM thinks about retention. You want teams to choose Prism because it is the fastest, safest way to ship an agent, not because they are told to.
• 'Founder Mode' Mindset: Entrepreneurial ownership, creative problem-solving, and a willingness to challenge the status quo, including how your own team is organized and staffed.
• Agentic Prototyping Pro: You don't wait for a build team. You stand up working prototypes and reference agents yourself using agentic tooling, put them in front of builders and customers quickly, and let evidence drive the next move.
• Spec-and-Eval Fluency: You can turn a fuzzy outcome into a crisp spec and a set of evals an agent fleet can be measured against, and you treat those evals as the contract for quality, both for Prism and for what others build on it.
• Strategic Direction & Prioritization: You set the vision and force-rank the bets, tying every bet to a workflow, a value pool, a moat, and a monetization path.

Duties & Responsibilities

• Strategic Direction & Roadmap: Own the vision and multi-year roadmap for Prism as a platform, its core agents, and its architecture. Sequence the roadmap with explicit dependencies across the data foundation, governance, and the ERP, and decide what ships first.
• Build the Core: Own Prism's core agents, the agent framework, and the architecture that lets agents act safely and scale: retrieval, ontology, the execution and governance model, and the surfaces other builders depend on.
• Scale the Platform (1-to-100): Make Prism a platform other teams build on. Own developer experience, golden paths, self-service onboarding, SDK/framework surfaces, extensibility, and the adoption metrics that prove it is working. Enable internal Epicor teams (Agent Foundry) and customers/partners (bring-your-own and custom agents) to build, and prevent duplicate, one-off builds.
• Outcome-Based Monetization & ROI: Help move Prism's commercial model from seat-based pricing toward usage- and outcome-based pricing (pricing the work, not the seat), including how platform usage and agents built on Prism are metered. Build the willingness-to-pay, value, and ROI analysis behind it, and be explicit about cannibalization trade-offs.
• Governance as Product: Treat approvals, policy, audit trails, reversibility, identity, and multi-tenant safety as product capabilities, for Prism's own agents and for everything built on the framework. Make "responsibility as moat" operational in the roadmap.
• Frontier-Team Leadership: Lead small, agent-augmented teams. Direct agent fleets against governed guardrails, hold clear decision rights, and protect the expert nuclei (architecture, evals, data, reliability) that make broad ownership sustainable.
• Quality by Design: Shift quality left. Define acceptance as eval evidence and outcome metrics captured in the workflow, not as a late gate, and ship that discipline as part of the framework so teams building on Prism inherit it.
• Go-to-Market & Developer Enablement: Partner with Product Marketing on go-to-market for the platform and the agents: reference customers, beta programs, sales and developer enablement, demo kits, and field narrative, leading with proof and risk absorption, not hype.
• Customer & Developer Advisory: Lead advisory engagement with both end customers and the internal/partner developers building on Prism, validate direction, and design the proactive, adoption-driving success model that AI platforms require, with continuous ROI demonstration.
• Trend Monitoring & Reuse: Stay current on agentic AI, retrieval, evals, ontology, and MCP-era integration (client, server, and control-point positioning). Fold emerging capability into the roadmap and the shared catalog so the portfolio compounds instead of duplicating.

Knowledge, Skills & Abilities

• Expert Product Management: Proven command of customer validation, prioritization, lifecycle management, and strategic roadmapping for complex products.
• Platform & Ecosystem Product Management: Demonstrated skill building products that other builders build on: developer experience, APIs/SDKs, extensibility, golden paths, and adoption, with the judgment for what to centralize versus leave local.
• Applied AI Acumen: Strong working knowledge of LLMs, RAG/retrieval, agents and agent orchestration, evals, and AI UX, enough to make architecture and trade-off decisions, not just sponsor them.
• Agentic Ways of Working: Comfort operating in a spec/eval/control-plane model, directing agent fleets, and prototyping directly. Familiarity with role convergence across Product/Dev/UX/Data Science and how to make broadened ownership work without role ambiguity.
• Analytical & Financial Expertise: Data-driven decision-making and the financial fluency to build usage/outcome pricing and rigorous ROI cases.
• Governance & Trust: Understanding of how identity, auditability, controls, and reversibility translate into customer trust and competitive defensibility, for a platform that hosts others' agents as well as your own.
• Leadership & Communication: Ability to lead converged, cross-functional teams and to communicate a clear, credible, non-hype platform vision to executives, customers, builders, and the field.
• Agile & SDLC + Cloud/SaaS: Familiarity with modern, AI-assisted delivery, shift-left quality, and scalable SaaS / cloud platforms. Previous ERP experience is a plus.

Required Qualifications

• Experience: 12+ years in product management with a track record of launching and scaling technology products, preferably in AI, machine learning, or related fields.
• AI Domain Depth: 4+ years of specialized experience in the AI domain, with demonstrated technical and market understanding.
• Education: Bachelor's degree in Computer Science, Engineering, Business, or a related field (or equivalent experience). Advanced degrees (MBA, Master's in AI/ML) are a plus.

Additional Qualifications

• Platform / Ecosystem Product Experience: Shipped developer-facing platforms, APIs/SDKs, or extensibility frameworks that internal or third-party teams built on, with adoption to show for it.
• Hands-On Agentic Tooling: Practical experience with prompt engineering, RAG, evals, and agent orchestration, and ideally with agentic coding tools used to prototype and ship.
• Programming Skills: Working proficiency in a language such as Python or C#, enough to read, prototype, and validate approaches with engineering and to direct agents credibly.
• Data & Integration Exposure: Familiarity with ETL / data-pipeline concepts, ontology/knowledge-graph thinking, and the MCP-era integration landscape.
• ML Frameworks: Exposure to frameworks such as TensorFlow, PyTorch, or scikit-learn is a plus.

This role is for a leader who wants to turn a working set of agents into a platform the whole company and its ecosystem build on: owning the core agents and architecture, and scaling the developer experience, governance, and reuse that take Prism from 1 to 100.

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.

Range:

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: