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About This Role
Overview:
The AI Product Owner owns the product definition, delivery rhythm, and value realization for internal AI capabilities. This role bridges business stakeholders, AI engineering, architecture, data/platform teams, security, legal, change management, and operations to turn high\-value workflows into production\-ready AI products.
The role is intentionally both product\-led and technically fluent. The AI Product Owner does not need to be the primary AI engineer, but must understand enough about LLMs, agentic workflows, retrieval, data readiness, evaluations, guardrails, observability, and cost controls to shape strong requirements, challenge weak assumptions, and make delivery tradeoffs visible.
This is also a hands\-on builder role. The AI Product Owner should be able to use tools such as Codex, Claude / Claude Code, and comparable AI development environments to prototype workflows, inspect code and data shapes, test prompts and agent behaviors, validate technical assumptions, and create lightweight working examples that help business and engineering teams move faster.
Responsibilities:
Product Strategy and Roadmap
- Own the product vision, roadmap, release plan, and prioritized backlog for assigned AI products, copilots, agents, automation patterns, and internal AI capabilities.
- Convert business problems into AI product opportunities with clear users, jobs to be done, value hypotheses, constraints, risks, and measurable outcomes.
- Partner with business owners to separate viable production use cases from experiments, demos, and low\-value automation requests.
- Maintain a clear product source of truth covering active use cases, owners, status, blockers, next decisions, value potential, launch readiness, and governance posture.
- Drive prioritization using business value, user pain, feasibility, data readiness, security risk, operational complexity, and strategic alignment.
AI\-First Requirements and Solution Shaping
- Translate business workflows into product requirements for AI assistants, copilots, agents, retrieval\-augmented generation, document understanding, generative analytics, workflow automation, and human\-in\-the\-loop experiences.
- Write PRDs, user stories, acceptance criteria, workflow maps, model behavior requirements, evaluation criteria, launch requirements, and operational runbooks.
- Define where AI should assist, automate, escalate, or defer to a human, including confidence thresholds, exception paths, approval flows, and audit requirements.
- Partner with AI engineering and architecture to shape requirements for prompts, tools/functions, data sources, retrieval design, structured outputs, model routing, integrations, and telemetry.
- Ensure requirements cover data access, privacy, tenant boundaries, retention, security controls, compliance constraints, and operational support needs.
Hands\-On AI Building and Technical Prototyping
- Use Codex, Claude / Claude Code, and comparable AI\-enabled development tools to build lightweight prototypes, prompt flows, agent workflows, evaluation examples, workflow automations, and proof\-of\-concept experiences.
- Get hands\-on with product discovery and feasibility testing by inspecting code, APIs, logs, data schemas, sample records, model outputs, retrieval results, and telemetry where appropriate.
- Create working artifacts that clarify requirements, accelerate engineering alignment, and help stakeholders see what a proposed AI capability would actually do.
- Test model behavior, prompt/tool interactions, edge cases, failure modes, guardrail needs, and human\-in\-the\-loop escalation patterns before work is scaled into production.
- Know the boundary between prototype and production: use hands\-on building to reduce ambiguity and risk, while partnering with engineering for secure, maintainable, supported implementation.
Delivery Execution
- Serve as day\-to\-day product owner for agile delivery teams building internal AI products and shared AI capabilities.
- Manage backlog grooming, sprint planning inputs, dependency tracking, acceptance review, release readiness, stakeholder demos, and post\-launch iteration.
- Coordinate across AI engineers, architects, data engineers, platform teams, application teams, UX/design, Legal, Security, GRC, support, and functional business owners.
- Keep delivery decisions explicit: scope, tradeoffs, risks, dependencies, assumptions, unresolved questions, and decision owners.
- Help teams choose the right build, buy, partner, or platform path based on speed, maintainability, governance, cost, and strategic leverage.
- Translate continuously between business and technical teams so product priorities, technical constraints, user impact, risk, and executive decisions stay aligned.
Evaluation, Quality, and Responsible AI
- Define product success metrics for each AI capability, including accuracy, relevance, task completion, cycle\-time reduction, user satisfaction, adoption, cost, latency, and business impact.
- Establish evaluation sets, test scenarios, red\-team cases, human review workflows, and launch gates appropriate to the risk level of each use case.
- Partner with engineering to monitor hallucination risk, prompt/tool failures, retrieval quality, model drift, latency, token usage, cost, user feedback, and incident patterns.
- Ensure AI products include guardrails, fallback behavior, explainability where needed, safe escalation paths, logging, auditability, and support handoffs.
- Drive responsible AI practices in partnership with Legal, Security, Privacy, Risk, Architecture, and business stakeholders.
Adoption, Enablement, and Value Realization
- Own launch planning, stakeholder readiness, training, communications, adoption tracking, and user feedback loops for assigned AI products.
- Partner with AI COE enablement efforts to help teams use AI responsibly and effectively, including playbooks, examples, office hours, and repeatable patterns.
- Translate product usage and outcome data into clear executive updates, recommendations, and next\-step decisions.
- Track value realization after launch, including productivity gains, quality improvements, risk reduction, revenue impact, cost avoidance, and lessons learned.
- Identify when products should scale, pause, pivot, retire, or move into a shared platform capability.
Portfolio Governance and Operating Rhythm
- Maintain the operating cadence for assigned AI products: intake, prioritization, delivery status, governance review, launch readiness, adoption review, and value tracking.
- Surface blockers early, especially data readiness, system integration, security/privacy approvals, stakeholder alignment, support model gaps, and unclear ownership.
- Prepare concise status updates for product, technology, functional, and executive stakeholders.
- Contribute to AI COE standards for product intake, backlog quality, evaluation readiness, release criteria, governance evidence, and value measurement.
- Help create reusable templates and patterns so future AI product work becomes faster, safer, and more consistent.
Qualifications:
- 6\+ years of experience in product ownership, product management, technical program management, business systems delivery, digital transformation, or comparable product delivery roles.
- 2\+ years of experience with AI, machine learning, data products, automation, analytics products, or emerging technology delivery; direct generative AI experience preferred.
- Strong technical fluency with modern AI product patterns, including LLMs, prompt design, RAG, vector search, agent/tool\-calling workflows, APIs, data pipelines, structured outputs, and telemetry.
- Demonstrated hands\-on ability to use AI\-assisted building tools such as Codex, Claude / Claude Code, or comparable platforms to prototype, inspect technical artifacts, test assumptions, and accelerate product discovery.
- Ability to work directly with technical materials such as APIs, logs, schemas, sample datasets, model outputs, prompts, evaluation results, and lightweight scripts or configurations.
- Proven ability to write clear product requirements, user stories, acceptance criteria, workflow maps, launch plans, and executive\-ready status updates.
- Experience managing agile backlogs and delivery tradeoffs across engineering, architecture, business, security, legal, data, and operations stakeholders.
- Strong analytical judgment with the ability to define product metrics, interpret usage/outcome data, and separate activity from business value.
- Practical understanding of privacy, security, access control, auditability, responsible AI, and operational risk in enterprise environments.
- Executive presence and excellent communication skills, with the ability to translate technical constraints into business decisions and business needs into technical requirements.
- Demonstrated ability to operate in ambiguous, fast\-moving environments without losing rigor around scope, ownership, evidence, and follow\-through.
Preferred Qualifications
- Hands\-on experience with enterprise AI platforms, LLM APIs, agent frameworks, AI copilots, AI assistants, RAG systems, document intelligence, workflow automation, or model evaluation tools.
- Familiarity with Microsoft 365, Teams, SharePoint, Azure DevOps, Power Platform, Copilot\-related tooling, Azure AI services, or comparable enterprise collaboration and delivery platforms.
- Experience with tools or patterns such as OpenAI, Anthropic, Gemini, LangChain, LlamaIndex, OpenAI Agents SDK, Google ADK, MCP, vector databases, LangSmith, OpenTelemetry, RAGAS, or AI eval frameworks.
- Experience in enterprise SaaS, property technology, finance operations, customer operations, sales operations, support operations, or regulated business workflows.
- Ability to use SQL, BI tools, scripting, low\-code tools, or technical prototyping methods to inspect data, test assumptions, and accelerate discovery.
- Experience building or operating a Center of Excellence, platform product model, product operations function, governance process, or internal enablement program.
- Familiarity with AI\-enabled software development tools such as Codex, Claude / Claude Code, Cursor, GitHub Copilot, or comparable developer/productivity platforms.
Success Measures
First 90 Days
- Establish a trusted source of truth for assigned AI products and use cases, including owners, status, blockers, next decisions, governance posture, and value hypotheses.
- Convert priority use cases into clear PRDs, user stories, acceptance criteria, evaluation plans, and launch\-readiness criteria.
- Create a working intake and prioritization rhythm with business stakeholders, AI engineering, architecture, security, legal, data/platform, and operations.
- Identify the top delivery risks across data access, integrations, privacy/security, evaluation readiness, adoption, and support model ownership.
First 180 Days
- Deliver multiple production\-ready AI product increments or shared AI capability releases with documented evaluation results, launch plans, adoption tracking, and support paths.
- Demonstrate measurable value through adoption, time saved, quality improvement, cycle\-time reduction, user satisfaction, risk reduction, cost control, or other agreed business outcomes.
- Mature the backlog from request collection into an evidence\-based product roadmap tied to business outcomes and AI COE standards.
- Establish repeatable product templates for AI requirements, evaluation plans, release readiness, governance evidence, and value realization.
Role Boundaries
- The AI Product Owner owns product direction, requirements, backlog quality, acceptance criteria, launch readiness, adoption, and value measurement for assigned AI products.
- AI engineering and architecture own technical implementation, platform design, engineering standards, and production technical operations, with the AI Product Owner shaping requirements and tradeoffs.
- Functional business owners own process decisions, business adoption, and outcome accountability, with the AI Product Owner making decisions, risks, and dependencies visible.
- Legal, Security, Privacy, Risk, and Architecture remain decision owners for their approval gates; the AI Product Owner ensures those gates are planned, evidenced, and not treated as afterthoughts.
Candidate Profile
- The strongest candidate will combine product judgment, hands\-on technical capability, operational discipline, executive presence, and strong communication. They should be comfortable working with engineers on model behavior, retrieval quality, eval design, APIs, telemetry, and guardrails, while also working with business leaders on workflow redesign, adoption, prioritization, value, and change management.
- This is not a pure backlog administrator role. It requires a product owner who can get hands\-on with AI tools, build enough to reduce ambiguity, translate effectively between executives and technical teams, help the organization decide where AI should be used, where it should not be used, how to prove it works, and how to scale it safely.
*SALARY AND BENEFITS*
- *RealPage provides a competitive salary package along with a comprehensive benefit plan that includes:*
- *Health, dental, and vision insurance.*
- *Retirement savings plan with company match.*
- *Paid time off and holidays.*
- *Professional development opportunities.*
- *Performance\-based bonus based on position.*
*Compensation may vary depending on your location, qualifications including job\-related education, training, experience, licensure, and certification, that could result at a level outside of these ranges. Certain roles are eligible for additional rewards, including annual bonus, and sales incentives depending on the terms of the applicable plan and role as well as individual performance.*
*Equal Opportunity Employer: RealPage Company is an equal opportunity employer and committed to creating an inclusive environment for all employees. \#LI\-JL1 \#LI\-HYBRID*
Pay Range: USD $157,600\.00 \- USD $268,400\.00 /Yr.
Salary Context
This $157K-$268K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At RealPage Inc, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $157K to $268K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
RealPage Inc AI Hiring
RealPage Inc has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Agent Developer. Based in Richardson, TX, US. Compensation range: $180K - $268K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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