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About This Role
POSITION SUMMARY
The Senior AI Product Manager (AI PM) will own the strategy, roadmap, and delivery of AI\-powered products and capabilities that improve operational efficiency, user experience, and decision\-making across the business. This role is responsible for identifying high\-value AI opportunities, translating them into clear product requirements, aligning cross\-functional stakeholders, and partnering closely with Engineering, Data/ML, Security, and Operations to deliver AI solutions that are scalable, trustworthy, and measurable. This role will support both internal and resident facing AI initiatives; scope will evolve based on business priorities and validated use cases.
ESSENTIAL DUTIES
AI Product Strategy \& Roadmap
- Define and own the vision, roadmap, and success metrics for AI products (e.g., workflow automation, predictive insights, document intelligence, conversational/agentic experiences).
- Identify and prioritize AI use cases that deliver measurable business outcomes (cost reduction, cycle\-time improvements, risk reduction, revenue lift, employee productivity).
- Conduct discovery with business stakeholders and end users to validate pain points and value hypotheses.
Product Execution \& Delivery
- Translate validated AI opportunities into well defined product requirements and a prioritized delivery backlog, with clear acceptance criteria covering quality, safety, and performance.
- Partner with delivery teams to plan and execute work using sprint based or flow based (Kanban) approaches, ensuring predictable delivery and business readiness.
- Ensure successful business adoption by partnering with end users, training teams, and operational leaders to drive measurable outcomes post\-launch.
- Ensure the product is “operationally real,” including training, rollout planning, support readiness, and documentation.
AI/ML Lifecycle, Governance \& Trust
- Define product performance metrics such as accuracy, latency, cost\-to\-serve, user adoption, retention, and task success rate.
- Establish ongoing monitoring and feedback mechanisms to ensure product reliability and effectiveness, including triggers for updates and improvements in collaboration with Engineering.
- Ensure responsible practices by implementing privacy, security, fairness checks where appropriate, maintaining explainability standards, and supporting auditability.
- Work closely with Security and Compliance teams to guarantee data handling, permissions, and governance adhere to enterprise requirements.
Stakeholder Alignment \& Communication
- Serve as the cross\-functional “translator” between business outcomes and technical implementation.
- Communicate roadmap progress, risks, tradeoffs, and decisions clearly to executives and operational leaders.
- Build alignment across teams and drive decisions in ambiguous problem spaces.
- Influences broader AI strategy and contributes to long\-term platform and capability planning across the organization.
Vendor / Platform Partnership (as applicable)
- Evaluate and manage third\-party AI vendors/tools (LLM providers, OCR/doc intelligence, MLOps platforms).
- Support procurement, contract negotiation inputs, and performance management against KPIs/SLA expectations.
Additional Duties: Tasks or duties not outlined in this job description may be required to contribute to the organization's success and efficiency.
QUALIFICATIONS
Education \& Experience:
- 5\+ years in Product Management (or equivalent), with at least 2\+ years delivering AI/ML or GenAI\-enabled products into production.
- Demonstrated experience taking products from discovery to launch, with measurable outcomes.
- Strong analytical skills and experience defining/using metrics to drive roadmap prioritization.
- Strong stakeholder management and executive communication skills.
- Bachelor’s degree required (STEM preferred) or equivalent experience.
Preferred Qualifications
- Experience with LLM/GenAI product patterns (RAG, tool/function calling, agent workflows), experimentation, and evaluation.
- Familiarity with MLOps and production AI considerations (monitoring, drift, retraining, safety guardrails).
- Experience with data governance concepts, integration patterns, APIs, and enterprise platforms.
- Experience in real estate, property management, finance/ops workflows, or regulated environments.
The Benefits of Employment
- Employee referral payment program
- Educational Enhancement Program
- Tuition Reimbursement
- Medical, Dental, and Vision benefits
- Life/ AD\&D Insurance
- Paid Parental Leave
- Long and short term disability
- Retirement Plan \- 401(k) Plan
- Volunteer \& Community Service Opportunities
- Discount on an apartment at any one of our properties
- Brand new amenity space that includes a full gym/fitness center and golf simulator
Salary range: $150,000 \- $200,000/yr
If you are hired at Morgan Properties, your overall compensation package will also be determined based on factors such as geographic location, skills, education, and/or experience which may result in total compensation outside of this range.
Get To Know Us:
Established in 1985 by Mitchell Morgan, Morgan Properties is a national real estate investment and management company headquartered in Conshohocken, Pennsylvania. Jonathan and Jason Morgan represent the next\-generation leaders growing the platform and overseeing the business operations.
Morgan Properties and its affiliates currently own and manage a multifamily portfolio comprised of more than 400 apartment communities and over 110,000 units located in 22 states. The Company is among the three largest multifamily owners in the nation and the largest in Pennsylvania, Maryland, and New York. With over 2,600 employees, Morgan Properties prides itself on its quick decision\-making capabilities, strong capital relationships, and proven operational expertise.
Salary Context
This $150K-$200K 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 Morgan Properties, 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 ($175K) sits 18% below the category median. Disclosed range: $150K to $200K.
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.
Morgan Properties AI Hiring
Morgan Properties has 1 open AI role right now. They're hiring across AI Product Manager. Based in Conshohocken, PA, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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.
Frequently Asked Questions
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