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About This Role
Expedient is a full\-stack technology services provider that helps mid\-market and enterprise organizations modernize their infrastructure, manage their data, and deploy AI safely at scale. With 25 years in business, a 99 percent client retention rate, a 100 percent uptime SLA, and 200\+ active technology certifications, Expedient delivers managed cloud, data, and AI services under a single operating model: Intelligent Infrastructure.
AI CTRL is Expedient's enterprise AI platform. It is the governed environment through which every prompt, every response, and every automated AI workflow inside a customer's organization is routed, monitored, and controlled. The platform is organized around six pillars: Secure AI Gateway, Multi\-Model Chat, Compliance and Observability, AI Data Connectors, Agentic Workflow Engine, and Private Model Hosting.
About The Role:
The Agentic Workflow Engine is where customers move from chat\-as\-tool to AI\-as\-operator. It turns AI access into automated business outcomes and delivers the highest\-value customer results across the AI CTRL portfolio.
The Principal Product Manager owns the Agentic Workflow Engine end to end. The role spans product strategy, roadmap, requirements, packaging, pricing, launch, sales enablement, marketing alignment, commercial performance, and the AI CTRL Development Partner Program. It is a hybrid product management, product marketing, and ecosystem role for a senior operator who has shipped workflow or agentic products before and runs a partner program with the same discipline as a roadmap.
This is a role for someone who runs a roadmap with conviction, makes calls when the data is imperfect, builds agents themselves on a workbench, and elevates the work of the teams around them.
What You Will Do:
Product Strategy and Roadmap
- Own the multi\-quarter roadmap for AI CTRL Agentic Workflow Engine, balancing customer demand, competitive pressure, partner input, and engineering capacity.
- Own P\&L for the Agentic Workflow Engine product line: revenue, gross margin, consumption growth, and unit economics.
- Translate the product vision into a prioritized backlog of agent patterns, tool integrations, workflow capabilities, and platform investments.
- Maintain a living product definition that aligns engineering, GTM, partners, and customer success on what is shipping, what is in Early Acceptance, and what is committed.
- Conduct ongoing competitive analysis of the agentic AI and workflow automation landscape, and surface inflection points that should shift the roadmap.
Product Development and Execution
- Write clear user stories, acceptance criteria, and product requirements for an agentic product built on a curated infrastructure stack.
- Define agent patterns, tool\-use contracts, evaluation frameworks, and guardrails. Set the standard for what good looks like before engineering writes the code.
- Partner with engineering leadership on release planning, sprint priorities, and feasibility tradeoffs.
- Operate inside a modern SDLC and apply AI\-assisted development practices where they accelerate the team.
- Drive the Early Acceptance\-to\-GA promotion process for new agent patterns, integrations, and workflow primitives.
- Own the product definition document, SKU catalog, supported model library, agent template catalog, and integration inventory as living artifacts.
Go\-to\-Market and Product Marketing
- Develop product positioning, packaging, and pricing recommendations for an emerging category, with claims that hold up under sales pressure and competitive comparison.
- Quantify and tell the ROI story of agentic workflows. Build a case\-study cadence so every quarter produces customer\-validated outcomes the field can sell with.
- Author the messaging framework for the Agentic Workflow Engine. Partner with marketing on campaign briefs, web copy, demand generation assets, and event positioning.
- Define the launch plan for each major release, including internal readiness, customer communications, partner enablement, and field activation.
- Maintain the product narrative across sales decks, solution briefs, agent pattern catalogs, and the field\-facing Product Guide.
Sales Enablement and Field Partnership
- Serve as the product authority for the field on a consultative sale. Run enablement sessions, hold office hours, and travel for high\-value deals when needed.
- Co\-own the sales motion with Sales Enablement: qualification criteria, agentic discovery questions, demo scripts, objection handling, and competitive battle cards.
- Sit in on customer discovery sessions, agent design workshops, and pilot reviews to maintain direct contact with the buyer, the builder, and the end user.
- Build feedback loops with Solutions Architects, Customer Success, and the AI Outcomes Team so field signal translates into roadmap input on a known cadence.
Development Partner Program
- Own the AI CTRL Development Partner Program end to end: design partners, delivery partners (systems integrators and consultancies), and ecosystem developers building on the Agentic Workflow Engine.
- Define partner tiers, certification paths, enablement curriculum, and joint\-delivery models. Stand up the supporting artifacts (partner portal content, technical guides, sandbox access, sample agents) so partners can be productive without continuous Expedient hand\-holding.
- Recruit and onboard design partners for net\-new agent patterns. Run those engagements as structured product discovery cycles, not custom services projects.
- Build and curate the agent template library with partner contributions, source\-of\-truth review, and version discipline.
- Track partner\-sourced revenue, partner\-attached deal size, certified partner count, and time\-to\-first\-deployed\-agent as program success metrics.
- Close the feedback loop. Partner\-surfaced patterns, gaps, and customer signal feed directly into the roadmap on a known cadence.
Practice Leadership
- Define how agent patterns are designed, how evaluations are built, how Early Acceptance\-to\-GA decisions are made, and how partner work feeds the roadmap.
- Mentor product managers across the AI CTRL portfolio and partner with Engineering and GTM leaders to elevate cross\-functional discipline.
- Represent AI CTRL externally at customer briefings, partner sessions, analyst conversations, and industry events on agentic AI.
- Build the feedback loop between field signal, partner signal, customer outcomes, and product investment.
What We Are Looking For:
Required Qualifications
- 10\+ years of B2B product management experience, including at least 3 years owning a workflow automation, low\-code, RPA, iPaaS, or agentic AI product.
- Demonstrated ability to operate as a hybrid product manager and product marketer across roadmap, requirements, positioning, packaging, and launch.
- Track record of shipping at least one product line from concept to material revenue, with documented ownership of commercial outcomes.
- Hands\-on workflow and agent design experience. You have built workflows and agents on Retool, n8n, Dify, Flowise, or comparable platforms, not just specified them.
- Track record of running a development partner, design partner, or developer ecosystem program with measurable outcomes (certified partners, partner\-sourced revenue, or shipped community contributions).
- Working knowledge of modern infrastructure: Kubernetes, identity (SSO, RBAC), observability, vector stores, and the LLM provider landscape.
- Hands\-on fluency with AI tooling. You use AI products daily, understand the difference between RAG and agentic workflows, and can debug a problem with engineering.
- Experience running a public\-facing roadmap, including the discipline to say no, defer, or kill a feature when the data does not support it.
- Comfort with commercial ownership: pricing decisions, deal economics, and forecast accountability.
- Excellent written and verbal communication. You can produce a one\-pager, lead an engineering standup, and present to a Sales VP in the same day.
- Bias to action. You make decisions when the information is imperfect, communicate the rationale clearly, and adjust when new data arrives.
- Experience operating in a matrixed organization and influencing without direct authority.
Preferred Qualifications
- Prior experience in a managed services, cloud, CX, or SaaS environment.
- Experience standing up product, partner, or developer relations functions where none existed.
- Familiarity with the workflow and agent platform landscape: Retool, n8n, Dify, Flowise, LangGraph, CrewAI, Goose, Langfuse, or comparable open\-source and commercial tools.
- Working knowledge of React for extending workflow surfaces.
- Comfort with vibe coding and other AI\-assisted development practices that compress design and build cycles.
- Experience selling to or supporting mid\-market enterprises in the $50M to $2B revenue range.
- Background launching trial programs or self\-service order\-to\-cash flows.
- Track record of partnering with both channel and direct sales teams.
- Bachelor's degree in Computer Science, Engineering, Business, or a related field. MBA a plus but not required.
Location and Travel:
This role is hybrid out of one of Expedient's offices in Pittsburgh, PA, Cleveland, OH, or Columbus, OH. Expect 20 to 30 percent travel for customer engagements, partner sessions, sales kickoffs, and industry events.
Salary for this position is directly related to your own experience, knowledge, and skills. Estimated range for this role is $150,000 to $180,000
\#LI\-hybrid
WORKING FOR EXPEDIENT
We prioritize ongoing education and continuous innovation to remain at the forefront of the information technology landscape. Our commitment to learning is reflected in our comprehensive employee training and tuition reimbursement programs, which are driven by our employees and funded by Expedient 100%.
For our full\-time employees we offer an exceptional benefits package including three weeks of paid time off annually that increases with tenure plus your birthday off and a health holiday to be used for preventive care. We offer parental leave, top\-tier medical, dental, and vision, disability and life insurance, at an affordable rate, wellness engagement opportunities, and a 401(k) with a generous match.
We also recognize the importance of a comfortable and convenient work environment. We offer a hybrid work model for many roles, paid parking and other perks.
Expedient is an equal opportunity employer. Qualified applicants will receive fair and equitable consideration for employment without regard to their race, color, religion, national origin, gender, protected veteran status, disability, or any other characteristic protected by law.
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Salary Context
This $150K-$180K range is below the median for AI Product Manager roles in our dataset (median: $189K across 161 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 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Expedient, 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 583 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($165K) sits 23% below the category median. Disclosed range: $150K to $180K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Expedient AI Hiring
Expedient has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in US. Compensation range: $125K - $180K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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