Interested in this AI Product Manager role at MCGOUGH CONSTRUCTION?
Apply Now →About This Role
The Sr. AI Product Manager acts as the business-side owner of McGough's enterprise AI capability portfolio, ensuring that AI investments, platforms such as ChatGPT Enterprise and Procore Agent Builder, and emerging AI capabilities drive measurable business outcomes across the organization. This role partners closely with Digital Operations, IT/IS, Data Engineering, Analytics, HR, Legal, Enterprise Applications, and business leadership to define, deliver, and improve AI capabilities aligned with business goals.
This individual defines business justification for AI investments, owns the intake and prioritization of AI opportunities and use cases, manages the roadmap, monitors adoption and ROI, and acts as the bridge between business stakeholders and technical delivery teams. They maintain an enterprise view of AI opportunities, balancing productivity improvements, capability development, and business value.
Success in this role requires the ability to translate technical potential into business value, identify high-impact opportunities, prioritize investments, and drive adoption across the organization. In addition to roadmap ownership, this role leads AI enablement efforts, partnering with business leaders and technical teams to support training, adoption, and capability development across the workforce.
QUALIFICATIONS:
Required:
- 7+ years of experience in digital product management, enterprise platform ownership, or enterprise application strategy.
- 2+ years of experience owning or managing an internal digital product or platform with emerging technology components.
- Fluency in Large Language Models (LLMs) and generative AI capabilities, limitations, and common enterprise risks.
- Proven ability to lead cross-functional teams to define and deliver digital tools that align with business and workforce needs.
- Demonstrated success in building and managing a business-focused roadmap for enterprise platforms that align to broader business capabilities and adjacent systems.
- Experience running structured intake, prioritization, and value assessment processes; defining business use cases and building business cases.
- Experience partnering with IT and Deployment Program Managers to define scope, success metrics, and delivery milestones.
Strong communication, stakeholder engagement, and organizational influence skills; able to lead cross-functional initiatives and influence without authority. *
Preferred:
- Experience working with Data Science, Analytics, Data Engineering, Machine Learning, or Intelligent Automation teams.
- Background in organizational change management, digital transformation, or innovation programs.
- Experience developing business cases, value realization frameworks, or investment recommendations.
- Familiarity with construction or project-based operational workflows.
- Systems-oriented thinking with awareness of cross-platform dependencies and downstream impacts.
- Familiarity with Agile delivery models, digital transformation initiatives, and organizational change management.
Bachelor's or Master's degree in Computer Science, Information Technology, Business Administration, or related field, or equivalent experience. *
Skills:
- Strategic product thinking paired with strong execution discipline; able to define and defend investment priorities.
- Strong collaboration and facilitation skills; able to drive clarity and accountability in complex initiatives.
- Comfortable managing ambiguity, prioritization, and change across competing priorities.
- Comfortable leading enablement, adoption, and capability development initiatives across diverse user groups.
- Strong analytical capability to define, track, and communicate adoption, performance, and business value metrics.
- Technically fluent enough to challenge assumptions and validate feasibility with IT and vendors.
- Systems-oriented thinking, with awareness of cross-platform dependencies and downstream impacts.
- Financial acumen to support budgeting, forecasting, and business case development.
- Experience with project and product management tools (JIRA, Confluence, Smartsheet, MS Project, etc.).
Clear, confident communicator with executive presence and attention to detail. *
RESPONSIBILITIES AND TASKS:
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- Define, maintain, and communicate the enterprise AI roadmap across ChatGPT Enterprise, Procore Agent Builder, and related AI capabilities, in partnership with Program/Product Managers.
- Own the intake, evaluation, and prioritization of AI opportunities and use cases across the business; partner with business teams to identify high-impact opportunities and align investments to business outcomes.
- Partner with Product Managers, Product Owners, IT, and business leaders to establish program goals, success metrics, and expected business value.
- Develop business cases and value assessments that support prioritization and investment decisions.
- Evaluate platform capabilities, vendor offerings, and emerging technologies and guide scaling, expansion, or sunsetting decisions based on business value and impact.
- Maintain awareness of emerging AI capabilities and assess applicability to McGough business objectives and operational needs.
- Surface cross-functional tradeoffs, dependencies, risks, and impacts to Program Managers, Digital Operations leadership, and domain leadership when AI initiatives impact broader business outcomes.
- Partner with domain-level leadership to ensure AI roadmap decisions support coherent, end-to-end business capabilities.
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- Translate business challenges into clearly defined AI use cases, requirements, adoption strategies, and measurable outcomes.
- Partner with Product Managers, Business Analysts, Application Specialists, Data Engineering, Analytics, and technical teams to define and deliver AI-enabled capabilities.
- Evaluate business challenges and determine the most appropriate solution approach, including process improvement, analytics, automation, generative AI, predictive modeling, or application-based capabilities.
- Support pilot programs, proof-of-concepts, and experimentation efforts to validate business value and organizational readiness.
- Manage third-party vendors and service providers to ensure accountability to scope, outcomes, and SOWs.
- Collaborate with business and technical teams to ensure AI solutions integrate effectively into existing workflows, processes, and systems.
- Partner with Data Governance, IT, HR, Legal, and business leaders to ensure AI capabilities align with enterprise policies and responsible-use expectations.
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- Define what successful AI adoption looks like across user groups, departments, and business functions.
- Lead enterprise AI enablement strategies, including training programs, office hours, learning pathways, and champion networks.
- Create and maintain internal documentation, quick-start guides, playbooks, and use case repositories aligned to business processes.
- Reinforce expected behaviors and responsible-use workflows through communication, training, and ongoing engagement.
- Partner with business stakeholders, Program Managers, BAs, and IT to identify adoption gaps and adjust enablement strategies accordingly.
Gather user feedback, success stories, and lessons learned to continuously improve adoption and user experience. *
Value Delivery & Continuous Improvement:
- Establish KPIs and success metrics tied to adoption, utilization, productivity improvements, business outcomes, and value realization.
- Track program progress through defined metrics including adoption, usage, quality, satisfaction, and realized business value.
- Develop executive-level reporting and visibility into portfolio performance, risks, opportunities, and outcomes.
- Monitor and communicate realized business value from AI investments and recommend adjustments to portfolio priorities based on measured outcomes.
- Identify opportunities to improve, expand, retire, or scale AI capabilities based on measurable results and evolving business needs.
- Champion AI-specific delivery standards, documentation practices, and continuous improvement efforts in partnership with IT and Program Management.
Collaborate with vendors and delivery partners to ensure solutions align with business needs and deliver expected value. *
Additional Responsibilities:
- Actively contribute as a member of the Emerging Technology team, collaborating to support shared goals and objectives
- Attend training for personal and/or professional development
- Represent McGough professionally at all events, upholding company standards and serving as a positive ambassador
- Collaborate across departments and with external stakeholders to ensure cohesive project execution
- Actively support and participate in Lean events, promoting the McGough Way and fostering a culture of continuous improvement
Other duties as assigned *
OFFICE AND TRAVEL:
- Position can be remote or based in McGough’s Roseville MN or Raleigh, NC Offices.
Travel is expected to be approximately 25% consisting of quarterly trips to McGough’s headquarters for various training or team building activities. Additional travel to various national office locations may be required as determined by the individual and their manager. *
PHYSICAL REQUIREMENTS :
The physical requirements listed here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Position involves sitting for extended periods of time at employee’s workstation and during meetings as well as while traveling, either by plane or car. Employee needs to be able to lift to 20 pounds as frequently as needed to move objects; dexterity to write and manipulate computer keyboard and mouse; ability to hear and speak clearly; and ability to distinguish between colors on graphs and charts.
Employee may be required to visit construction jobsites which may expose the employee to dirt, dust, uneven surfaces, outdoor weather conditions and extreme temperatures.
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 MCGOUGH CONSTRUCTION, 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. Senior-level AI roles across all categories have a median of $229,300.
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.
MCGOUGH CONSTRUCTION AI Hiring
MCGOUGH CONSTRUCTION has 1 open AI role right now. They're hiring across AI Product Manager. Based in Raleigh, NC, 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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