Interested in this AI Product Manager role at PROCONEX?
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Proconex is the exclusive Emerson Impact Partner for Emerson Automation Solutions in the Mid-Atlantic region. We provide a full portfolio of Process Automation Control Systems, Valves and Control Devices, and related technologies and services to a range of customers—in the Life Sciences, Chemical, Power, Refining, Oil & Gas, Food & Beverage, Paper, Metals, and other industries, as well as commercial enterprises and campus facilities. With three strategically located offices, Proconex maintains a deep dedication to its 200+ employees, a sharp focus on safety and quality, and the drive to deliver.
Job Overview
The Product Manager, AI Platforms is responsible for defining, prioritizing, and advancing product strategy across Linque's industrial intelligence ecosystem, including AIME, Mobius, VisionAI, AI-enabled verification, AI-Ops/Agentic AI, and related platform capabilities. This individual will serve as the primary Product Owner for VisionAI and a strategic leader across the broader platform portfolio, translating customer needs, market insights, competitive intelligence, and business priorities into product roadmaps, requirements, release plans, and commercialization strategies.
Working closely with engineering, AI platform leadership, quality, sales, business development, and customer-facing teams, the Product Manager will drive VisionAI market readiness, productization efforts, customer adoption, and long-term platform growth. Success in this role requires strong customer empathy, commercial judgment, strategic communication skills, and the ability to influence cross-functional stakeholders without direct authority. This role brings a structured approach to problem solving, balances customer needs with technical feasibility and business value, and exists in a fast-paced environment where priorities evolve and rapid iteration are required.
Key Responsibilities
Product Strategy, Roadmap & Governance
- Own and maintain the product roadmap across the AIME/Mobius ecosystem and related platform capabilities, balancing long-term platform direction with near-term commercial priorities.
- Translate business strategy, customer requirements, and market needs into product themes, epics, user stories, release priorities, and measurable outcomes.
- Establish product governance processes for roadmap decisions, prioritization, release planning, and cross-functional alignment.
- Define boundaries between platform capabilities, configurable features, customer-specific development, and service offerings.
- Align product investments with recurring revenue growth, customer adoption, implementation scalability, and long-term competitive positioning.
Voice of Customer, Market Intelligence & Competitive Positioning
- Serve as the primary owner of customer, prospect, pilot, sales, support, implementation, and partner feedback.
- Monitor industry trends, customer buying behaviors, competitive offerings, technology advancements, and emerging opportunities across industrial software, VisionAI, AI-enabled verification, and connected operations.
- Translate customer and commercial feedback into actionable product priorities, roadmap decisions, packaging considerations, and market opportunities.
- Maintain a strong understanding of customer personas, use cases, buying criteria, value propositions, and operational pain points.
- Develop and maintain competitive intelligence that informs product strategy, differentiation, commercialization, and positioning.
VisionAI Product Development & Market Readiness
- Lead initiatives to define, validate, and scale VisionAI product offerings, converting customer pilots and emerging opportunities into repeatable, commercially viable solutions.
- Define VisionAI requirements, MVP scope, roadmap priorities, release readiness criteria, and commercialization milestones.
- Support productization of applications such as foam detection, visual inspection, AI-enabled verification, and industrial quality monitoring.
- Partner with engineering, AI platform, quality, and commercial teams to ensure VisionAI offerings are scalable, supportable, and aligned with customer requirements.
- Position VisionAI as both a standalone solution and an integrated capability within the broader AIME/Mobius ecosystem.
Cross-Functional Product Execution
- Partner with software, AI platform, and engineering leaders to align product strategy with technical architecture, platform capabilities, engineering capacity, and delivery sequencing.
- Support backlog refinement, sprint planning, requirements clarification, and acceptance criteria definition to ensure successful execution against roadmap priorities.
- Collaborate closely with Sales, Marketing, Customer Success, Support, Quality, and Operations teams to ensure products can be effectively sold, implemented, supported, and scaled.
- Facilitate tradeoff decisions balancing customer requests, revenue opportunities, scalability, technical debt, quality and compliance requirements, and long-term product strategy.
- Communicate roadmap priorities, product decisions, risks, dependencies, and release expectations across stakeholders.
Product Commercialization & Sales Enablement
- Develop product positioning, buyer personas, use cases, value propositions, messaging, and market narratives for AIME, Mobius, VisionAI, and related offerings.
- Support customer discovery, demonstrations, pilots, proof-of-concept initiatives, proposal development, and strategic sales pursuits.
- Create product briefs, release notes, enablement materials, launch plans, pricing inputs, product packaging concepts, and customer-facing collateral.
- Ensure product packaging and commercialization strategies consider implementation complexity, support readiness, scalability, and customer adoption requirements.
- Represent Linque's product organization in customer meetings, partner engagements, industry events, and internal strategic planning activities.
Basic Qualifications
- Bachelor's degree in Engineering, Computer Science, Business, Product Management, or a related field or equivalent experience.
- 5+ years of experience in product management, product strategy, software product ownership, industrial software, AI-enabled products, automation technology, manufacturing technology, or related technical-commercial roles.
- 2+ years of experience translating customer needs, market insights, and technical constraints into product roadmaps, requirements, release plans, and commercialization priorities.
- 2+ years of experience collaborating with software engineering, AI/ML, data, platform, hardware, automation, or industrial technology teams.
- 2+ years of experience with product roadmapping, prioritization, backlog management, and agile product execution.
- 2+ years of experience gathering and synthesizing Voice of Customer feedback, market intelligence, and competitive insights.
- 2+ years of experience supporting product launches, commercialization efforts, sales enablement activities, customer discovery, or go-to-market initiatives.
Preferred Qualifications
- Advanced degree in Engineering, Computer Science, Business, Product Management, or a related discipline.
- Experience supporting software products within industrial manufacturing, infrastructure, energy, utilities, pharmaceutical, life sciences, or other mission-critical environments.
- Life sciences, GMP-regulated, validation-oriented, or quality-focused manufacturing experience.
- Experience with VisionAI, machine vision, computer vision, AI-enabled verification, applied AI, or industrial AI solutions.
- Experience with hardware-enabled software, edge deployments, industrial cameras, sensors, automation systems, or connected plant-floor technologies.
- Experience supporting cloud, on-premises, and edge deployment models.
- Experience developing competitive intelligence, product packaging, pricing strategies, commercialization frameworks, and sales enablement programs.
- Proven track record of building strong relationships across technical and commercial teams while effectively representing products in customer-facing and executive-level environments.
We are proud to be an employee-owned organization. Through our ESOP, every team member shares in the growth and success of the company, fostering a culture of collaboration, accountability, and long-term rewards.
Proconex offers a standout culture, fantastic work environment, and has very competitive benefits to include premium medical, 401K match, tuition reimbursement, and other unique incentives.
When receiving e-mails from a Proconex or Valspec recruiter, you will only receive e-mails from addresses ending in @proconexdirect.com, @proconex.net or @valspec.net. Any other domains are impersonations and should be ignored.
Candidates must be legally eligible to work in the United States without company sponsorship. Also, we are not interested in working on a corp-to-corp basis with other companies at this time.
Any third-party unsolicited resume submission(s) will immediately become the property of Proconex. Proconex will not pay any fee to a submitting employment agency, person, or entity unless a signed agreement is established.
Proconex is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to sex, race, color, religion, national origin, age, sexual orientation, gender identity, disability, protected veteran status or any other protected class. We are committed to providing a professional environment free of any discrimination or harassment, and we are proud to be a Drug-Free Workplace.
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 9,079 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At PROCONEX, 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 947 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
PROCONEX AI Hiring
PROCONEX has 1 open AI role right now. They're hiring across AI Product Manager. Based in Phoenixville, PA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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