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About This Role
84.51° Overview:
84.51° is a retail data science, insights and media company. We help The Kroger Co., consumer packaged goods companies, agencies, publishers and affiliates create more personalized and valuable experiences for shoppers across the path to purchase.
Powered by cutting-edge science, we utilize first-party retail data from more than 62 million U.S. households sourced through the Kroger Plus loyalty card program to fuel a more customer-centric journey using 84.51° Insights, 84.51° Loyalty Marketing and our retail media advertising solution, Kroger Precision Marketing.
84.51° follows a 5‑day in‑office work schedule to support collaboration, alignment, and team connection.
Join us at 84.51°!
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84.51° is a retail data science, insights, and media company that helps brands grow by delivering smarter, more accountable marketing powered by Kroger's first-party data. Kroger Precision Marketing (KPM) is our commercial arm, enabling brands to connect with customers through insights, incentives, and media.
Role Summary
Kroger Precision Marketing (KPM) has launched an AI Platform domain to define how AI is built, scaled, and used across internal teams and external brand-facing platforms.
This Senior Product Manager role leads a cross-functional team across Data Science and Engineering and owns the roadmap for the shared foundation that helps KPM teams build trusted agents at scale. The north star for this foundation is to reduce the time and cost of building new AI capabilities by turning common infrastructure, context, tooling, and governance into reusable platform services. This foundation is what makes a unified KPM AI experience possible — one assistant, across every platform, workflow, and team, built once and delivered everywhere.
The work spans two layers. The infrastructure layer includes enterprise agent development and deployment patterns, shared chat and notification experiences, Agent-to-Agent (A2A) interactions, and Model Context Protocol (MCP) infrastructure. The intelligence and quality layer includes context-layer capabilities, agent reasoning and evaluation, LLM observability, feedback loop design, product analytics, and governance.
The role partners with the AI Platform Director to connect these capabilities to the broader domain vision while creating practical, reusable platform services that teams can build on without reinventing the same foundations.
Key Responsibilities
- Own the platform roadmap: translate the AI Platform vision into a clear roadmap, proactively define plans to move the work forward, and sequence foundational capabilities based on customer needs, business value, technical readiness, risk, cost, and reuse potential
- Scale reusable AI foundations: own shared agent infrastructure, context-layer capabilities, deployment patterns, tooling, and standards that help teams build new AI capabilities faster without duplicating platform wiring
- Know your builders: partner with product and agent builders to understand how they work, where they are blocked, and what shared capabilities would unlock the most value across the portfolio
- Pick the right bets: identify the highest-leverage platform investments, make explicit tradeoffs, and focus the team on the capabilities that unlock the most reuse, adoption, and downstream product impact
- Lead with market-informed judgment: stay current on AI trends, vendors, and competitor moves; form a clear point of view; seek input early; and adjust quickly when evidence points to a better approach
- Ship with momentum: drive delivery with Agile Delivery, Data Science, and Engineering by managing scope, dependencies, risks, tradeoffs, and timelines
- Drive adoption, quality, and impact: define rollout plans, success measures, agent quality evaluations, observability signals, and feedback loops that improve reliability, adoption, and business impact
- Enable the organization: educate teams on the platform, how to use it, and where it is going; rally stakeholders around the shared vision; and create the forums, documentation, and lightweight processes that help builders adopt the platform consistently
- Communicate clearly and build trust: communicate plans, progress, tradeoffs, risks, and decisions with clarity; raise issues early with context and options; and earn trust through strong follow-through and reliable decision-making
Minimum Qualifications
- Product management: 3–5 years of product management experience
- GenAI product delivery and quality: strong understanding of how LLM-powered and agentic products move from prototype to production, including prompt and context design, RAG, orchestration, tool integration, evaluation frameworks, LLM observability, feedback loops, governance, and rollout
- Platform and builder mindset: experience building shared platforms, internal developer tools, or reusable capabilities where product and engineering teams are the primary customers
- Stakeholder intake and roadmap tradeoffs: experience gathering needs from multiple stakeholders, reconciling competing priorities, and sequencing shared platform work under resource constraints
- Technical fluency: background in engineering, data science, analytics, or a highly technical product area, with the ability to go deep on architecture and delivery tradeoffs
- Communication: ability to translate technical complexity into clear decisions, tradeoffs, risks, and recommendations for business and technical audiences
- Leadership: a humble, direct leadership style, comfortable saying "I don't know," and confident leading toward an answer
- Operating judgment: ability to move quickly while respecting security, governance, and enterprise delivery standards
Preferred Qualifications
- B2B platform: experience building products for large, diverse internal or external user bases
- Industry: familiarity with retail analytics, retail media, adtech, and data-driven marketing platforms
- Experimentation and measurement: product analytics experience defining success metrics, selecting the right measures for the job (for example, using Google's HEART framework), and running experiments to quantify user and business impact
- AI evaluation and observability: familiarity with LLM evaluation frameworks, agent quality measurement, AI observability tooling, or feedback loop design
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Salary Context
This $125K-$170K range is in the lower quartile for AI Product Manager roles in our dataset (median: $193K across 93 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 8,943 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At 84.51°, 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 $220,200 based on 862 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($147K) sits 33% below the category median. Disclosed range: $125K to $170K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
84.51° AI Hiring
84.51° has 1 open AI role right now. They're hiring across AI Product Manager. Based in Cincinnati, OH, US. Compensation range: $170K - $170K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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