Top Tech Jobs & Startup Jobs

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.

Develops and governs Salesforce reports, dashboards, and commercial analytics supporting pipeline management, forecasting, revenue visibility, opportunity tracking, and executive decisions. The role investigates data discrepancies, maintains reporting governance, improves data quality, enables self-service analytics, supports Salesforce adoption, and identifies automation and AI-readiness opportunities. It partners with Sales, Finance, Product, Engineering, and leadership to translate business needs into scalable reporting 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.

Supports the strategy, roadmap, prioritization, backlog, and stakeholder alignment for data products enabling scalable analytics, self-service data use, and AI capabilities. Translates business needs into product requirements, features, user stories, and acceptance criteria. Uses customer research, analytics, and product metrics to guide decisions, while partnering with Engineering, Architecture, data stakeholders, and users to improve data quality, governance, discoverability, usability, adoption, and business value.

Lead the roadmap for an enterprise AI platform supporting reusable agent infrastructure, context capabilities, deployment patterns, evaluation, observability, governance, and feedback loops. Partner with Data Science, Engineering, and product builders to prioritize platform investments, manage delivery tradeoffs, drive adoption, measure impact, and establish reliable AI development standards across Kroger Precision Marketing.

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 a forward-deployed engineering function that enables enterprise adoption of AI coding tools. Embed with delivery teams, write software, document learnings, monitor adoption through observability tools, identify platform and policy gaps, maintain internal plugin marketplaces, manage SLAs and incidents, and participate in on-call and business-hours support. Collaborate with platform, security, infrastructure, product, and development teams to scale AI-enabled software delivery.

Lead applied AI initiatives by co-developing AI agents, embeddings-based solutions, and proprietary language-model applications with business teams. Identify AI use cases, develop rapid proofs of concept, mentor junior team members, create trainings and technical documentation, guide production implementations, troubleshoot integrations, and collaborate with engineering partners to deliver modular solutions.

Designs and delivers architectures for AI-enabled products across data platforms, APIs, cloud applications, machine learning workflows, and event-driven integrations. Partners with product, engineering, data science, security, and business teams to translate needs into technical designs, prototypes, implementation plans, and scalable production solutions. The role also defines API and governance patterns, evaluates emerging AI technologies, supports delivery teams, and mentors engineers on architecture and pragmatic technical execution.

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.