Jobs at 84.51°

Design, build, and maintain Databricks and cloud-based data pipelines and tooling to ingest, transform, store, and distribute large retail datasets. Implement automated testing, CI/CD, and pipeline orchestration, mentor junior engineers, collaborate on architecture and estimations, and drive improvements in data engineering practices and reliability.

Lead end-to-end client partnerships for 84.51° products, build digital media plans, prospect and grow CPG and agency relationships, drive revenue against quota, coordinate cross-functional execution, use insights and CRM to inform strategies, and support advertising campaign delivery. Requires frequent client engagement and travel.

Lead a Labs team as a hands-on engineer building hybrid AI/optimization systems: design ML-augmented optimization, prototype and productionize solutions, architect MLOps/CI-CD, benchmark solvers, mentor engineers, and collaborate with researchers and stakeholders to scale reusable components and APIs across Kroger.

Lead end-to-end growth for enterprise, mid-tier and agency clients—balancing new business acquisition with client success, retention, and expansion. Manage and develop a client-facing team, drive revenue planning and forecasting, negotiate complex deals, influence product roadmaps, and coordinate cross-functional execution to deliver data-driven media, incentives, and insights. Represent the company externally and stay current on retail media and digital marketing trends.

Own product strategy and execution for AI-powered SDLC tooling. Define roadmaps, translate developer workflows into requirements, deliver scalable AI developer tools, measure adoption and value, manage cross-functional agile teams, and ensure governance, security, and responsible AI practices.

Lead Applied AI product initiatives: identify and co-develop high-impact AI use cases, lead prototypes/MVPs with cross-functional teams, drive adoption of GenAI and AI Enablement capabilities, champion responsible and explainable AI, run AI ideation workshops, manage AI Spoke network, track business value, and own product responsibilities (OKRs, roadmaps, planning, Jira execution).

Lead product owner for the AI Gateway platform driving roadmap, prioritization, backlog management, cross-functional delivery, stakeholder management, and measurement of AI product value. Requires translating business needs into technical requirements, defining acceptance criteria, and partnering with engineering, UX, and data science to deliver AI/Generative AI solutions.

Provide technical leadership for search and recommender systems: design, prototype, and deploy production-ready ML solutions (including LLM and agent-based methods); define evaluation and experimentation frameworks; partner with engineering on real-time serving, feature pipelines, and monitoring; drive cross-functional alignment and mentor data scientists.

Lead design and implementation of Databricks/Azure data solutions to ingest, store, transform, and distribute large-scale retail data. Own features through the SDLC, mentor engineers, lead cross-functional collaboration, build REST APIs on Kubernetes, execute testing, and set technical strategy with Product, Data Science, and Architecture.

Design, build, and maintain Databricks and cloud-based data ingestion, storage, and distribution solutions. Implement ETL/warehousing, pipeline orchestration, CI/CD, automated testing, and mentor junior engineers while collaborating across architecture and scrum teams to deliver scalable data products.

Lead the Agentic Foundations engineering team to design, build, and operate enterprise-grade agentic platforms and tooling. Provide technical and operational leadership across AI agent registries, deployment, monitoring, reliability, and developer-facing abstractions while hiring and managing engineering teams and offshore reliability resources.

Own and evolve enterprise data platform capabilities for data quality, freshness, reliability, and dependency discovery. Define 12-month roadmaps, translate business goals into product requirements, partner with engineering and data teams for delivery, monitor adoption and product health, maintain core product artifacts, and drive cross-functional adoption and enablement.

Lead design and delivery of cloud-native, AI-ready data architectures and semantic layers. Define data models, integration patterns, governance, and migration roadmaps. Guide technical decisions, build reference implementations, mentor teams, and ensure security, cost management, and observability for production-grade AI and analytics platforms.

Lead strategy and execution for an AI Platform domain: set vision, manage multiple product teams, define measurable outcomes, build operating systems and reusable capabilities, partner with engineering and commercial teams, drive adoption, and coach product leaders to deliver production-ready, responsible AI features that create business impact.

Lead strategy, execution, and operations for enterprise AI and technical education. Own AI learning roadmaps, design AI-native learning experiences, align cross-functional stakeholders, manage vendors and platforms, measure impact, and drive adoption and continuous improvement at scale.