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

#LI-EB1