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About This Role
At Cotality, we are driven by a single mission—to make the property industry faster, smarter, and more people\-centric. Cotality is the trusted source for property intelligence, with unmatched precision, depth, breadth, and insights across the entire ecosystem. Our talented team of 5,000 employees globally uses our network, scale, connectivity and technology to drive the largest asset class in the world. Join us as we work toward our vision of fueling a thriving global property ecosystem and a more resilient society.
Cotality is committed to cultivating a diverse and inclusive work culture that inspires innovation and bold thinking; it's a place where you can collaborate, feel valued, develop skills and directly impact the real estate economy. We know our people are our greatest asset. At Cotality, you can be yourself, lift people up and make an impact. By putting clients first and continuously innovating, we're working together to set the pace for unlocking new possibilities that better serve the property industry.
Job Description:
Role Summary
The Principal Technical Product Manager for AI Integration owns the strategy and delivery of Cotality Insurance's external\-facing AI integration layer. This includes the MCP server ecosystem, AI/API gateway, developer experience, and the platform that enables AI agents from clients and third\-party partners to discover, authenticate, and consume Cotality's insurance data products autonomously.
This is a technical product leadership role with a player\-coach model. You set the product direction, make architecture tradeoff decisions alongside Architecture, and drive execution through a dedicated engineering team and cross\-functional application team partners who own the underlying product APIs.
### Core Responsibilities
MCP Platform Strategy \& Roadmap. Define which Cotality data products get exposed as MCP tools, in what order, and with what capabilities. Align the connector roadmap with business priorities across Claims, Underwriting, Catastrophe Risk, and Contractor Solutions. Own the sequencing decisions — what ships this quarter, what's next, and why. Target cadence: one new MCP connector live per month.
Tool Schema \& Data Product Definition. Partner with product teams and the Data Architect to define what data gets exposed through each MCP tool — the fields, the boundaries, the descriptions that AI agents read to decide when and how to use a tool. This is the highest\-leverage work in the role. The quality of tool schemas directly determines whether an AI agent can use Cotality's data effectively or makes errors that damage client trust.
Architecture \& Technical Direction. Work with the Architect and engineering team to define gateway configurations, authentication flows, and the aggregation layer. Make tradeoff decisions — when to optimize for speed vs. extensibility, when to wrap an existing API vs. build a composite tool, when to ship and iterate vs. get it right the first time. You don't write the code, but you understand the architecture deeply enough to lead technical decisions.
Developer Experience. Own the end\-to\-end experience for external developers and AI platforms integrating with Cotality's MCP endpoints. This includes the developer portal, API documentation, sandbox environments, authentication guides, SDK examples, and onboarding workflows. You understand what good developer experience feels like because you've been the developer — you've integrated against third\-party APIs, read bad documentation, and know the difference between a portal that accelerates adoption and one that generates support tickets. Target: a developer or AI agent goes from zero to working Cotality data in under 60 minutes.
Data Provenance \& Trust. Own the strategy for ensuring data delivered through MCP tools is verifiable, auditable, and resistant to misattribution or hallucination by consuming AI agents. Define response metadata standards, logging requirements, and the guardrails that protect Cotality's brand when data flows through systems Cotality doesn't control. This includes near\-term controls (response signing, audit logs, server instructions) and the longer\-term innovation roadmap for data provenance.
Go\-to\-Market Coordination. Work with product marketing, sales engineering, and business development to position the MCP platform for carrier clients and AI platform partners. Support demos, pilot programs, and partner integrations.
Cross\-Functional Execution. Drive delivery through a dedicated MCP engineering team (MCP Architect, MCP Engineers, Data Architect) and application team partners who own the underlying product APIs. You define what needs to be built, set priorities, and shape tool designs. Application teams contribute significant implementation effort, particularly around exposing their product APIs as MCP\-ready services. Keeping multiple workstreams aligned and moving toward shared delivery timelines is a core part of the role.
Job Qualifications:
### Required
- 8\+ years in technical product management or architecture, with at least 3 years owning API platforms, developer tools, data products, or integration infrastructure
- Engineering background — you've written code professionally and carry an intuitive understanding of what makes a great developer experience from the consumer side, not just the provider side
- Demonstrated experience shipping developer\-facing or machine\-consumable products with measurable adoption metrics
- Strong understanding of API gateway patterns, OAuth/authentication flows, and how distributed systems communicate
- Working knowledge of how large language models consume tools — function calling, tool descriptions, context windows, and the failure modes that arise when AI agents interact with external data sources
- Experience defining data products for external consumption — what to expose, what to withhold, how to structure responses for different consumer types, and how to manage data quality and trust
- Track record of driving delivery through cross\-functional teams without direct reporting authority — influencing engineering, product, and business stakeholders to execute against a shared roadmap
- Exceptional written communication — you will write tool descriptions that AI agents read, developer documentation that humans read, strategy documents that leadership reads, and contract language that legal reviews
- Comfort with ambiguity and speed — this is a new category with no playbook, requiring decisions with incomplete information, shipping MVPs, and iterating rapidly
### Preferred
- Experience with specific API gateway technologies (Apigee, Kong, AWS API Gateway, or equivalent)
- Hands\-on experience with developer documentation and portal platforms — Mintlify, Swagger/OpenAPI, Redoc, ReadMe, or similar — and a strong opinion on what makes API documentation actually usable
- Familiarity with the Model Context Protocol (MCP) or equivalent agent\-tool interface standards
- Experience in insurance, financial services, or regulated data industries
- Understanding of data provenance, audit requirements, and compliance considerations in regulated environments
- Experience with pricing and packaging of API/data products (per\-query, tiered access, usage\-based models)
- Background in or exposure to AI/ML workflows, particularly how enterprises are deploying AI agents in production
### What Differentiates the Ideal Candidate
This role is not about managing a backlog and writing user stories. The ideal candidate is someone who thinks about how machines consume data — not just how humans use software. They understand that a tool description is an instruction set for an LLM, that a missing field in an API response can trigger hallucination in an agent, and that the developer experience for an AI platform partner looks fundamentally different from the developer experience for a human integration engineer.
They are comfortable being the bridge between deeply technical architecture discussions and business strategy conversations. They can explain to an engineer why a tool schema needs to be restructured for agent effectiveness, and they can explain to a VP of Sales why a carrier's AI agent using Cotality data is a stickier revenue relationship than a traditional API integration.
They see the opportunity: agent\-consumable insurance intelligence is a new product category, and the person in this role gets to define it.
Annual Pay Range:
111,900 \- 175,000 USD
Application Window:
This opportunity is expected to remain posted through the date identified below, subject to business needs.
Thrive with Cotality
At Cotality, we offer more than just a job, we provide a benefits experience designed to support your whole self. From a flexible working model to competitive time off and standout health coverage with meaningful perks and growth opportunities, our package is built to help you thrive at work and in life.
Highlights, depending on role classification, include:
- Time off: Generous PTO and 11 paid holidays, plus well\-being and volunteer time off.
- Family Support: Up to 16 weeks of fully paid parental leave and a baby stipend.
- Health: Multiple medical plan options with mental health and wellness support offerings.
- Retirement: 401(k) with company match and vesting after one year.
- Financial Perks: $400 annual well\-being stipend and tuition assistance up to $5,250\.
- Extras: Recognition Rewards, Referral bonuses, exclusive discounts and more!
Cotality is an Equal Opportunity employer committed to attracting and retaining the best\-qualified people available, without regard to race, color, religion, national origin, gender, sexual orientation, gender identity, age, disability or status as a veteran of the Armed Forces, or any other basis protected by federal, state or local law. Cotality maintains a Drug\-Free Workplace.
Cotality is fully committed to a work environment that embraces everyone’s unique contributions, experiences and values. We offer an empowered work environment that encourages creativity, initiative and professional growth and provides a competitive salary and benefits package. We are better together when we support and recognize our differences.
By providing your telephone number, you agree to receive automated (SMS) text messages at that number from Cotality regarding all matters related to your application and, if you are hired, your employment and company business. Message \& data rates may apply. You can opt out at any time by responding STOP or UNSUBSCRIBING and will automatically be opted out company\-wide.
Salary Context
This $111K-$175K range is in the lower quartile for AI Product Manager roles in our dataset (median: $185K across 167 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 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Cotality, 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 $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($143K) sits 34% below the category median. Disclosed range: $111K to $175K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Cotality AI Hiring
Cotality has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Dallas, TX, US, Milwaukee, WI, US. Compensation range: $130K - $190K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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