Director, Agentic Engineering (P4474)

84.51° · Cincinnati, Chicago · $162,000–$262,000 (stated in the posting)

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Director, Agentic Engineering

Grade G4 | Engineering | AI Enablement, 84.51° and Kroger Technology & Digital (KTD) | Reports to [Senior Director/VP, AI Enablement] | Location [Cincinnati, OH/Chicago, IL]

About 84.51 and the opportunity

84.51° is Kroger’s data science, insights and AI company. Kroger is a roughly $150B Fortune 20 retailer, and the software and AI products our teams build shape how that business serves its customers every day. This role is an enterprise-level opportunity to reinvent how software and AI products are built across 84.51°, KTD and Kroger, and to set the standard for how engineering at this scale works alongside AI agents.

The foundation is already in motion. Claude Code is generally available to technologists under an enterprise-managed configuration, GitHub Copilot and Microsoft 365 Copilot are in the hands of thousands of associates, Claude Cowork and Claude Design have waitlists of eager users, AI Hub is becoming the front door for “how to AI at Kroger”, and a managed agent platform and the first lighthouse projects are live. The next step is to turn strong tools and early wins into a measurable, repeatable, enterprise-wide way of building software.

Role Summary

The Director, Agentic engineering will move our organization from AI-assisted coding to a genuinely agentic engineering organization. You will redesign the end-to-end software development lifecycle (plan, design, build, test, deploy and operate) around AI agents, with people owning judgment at the critical gates: product intent, architecture, design and risk approval.

You will own the developer-facing AI ecosystem end to end: the AI SDLC operating model, the rollout and adoption of coding agents such as Claude Code, new autonomous agentic systems such as Devin, the AI Developer Portal that gives every builder a governed, self-service path to models, agents and tools, and the engineering experience surfaced through AI Hub. You will partner directly with the C-suite to shape the strategy and investment case and keep them informed of progress with clarity and candor. The goal is materially faster delivery, with quality, security, reliability and cost held at or better than today’s bar.

The AI Engineering Ecosystem You Will Lead

The table below summarizes the capabilities this role owns or shapes, and the mandate for each.

What You’ll Do

Strategy and Executive Partnership

• Build the multi-year agentic engineering strategy and investment case in partnership with the C-suite, sequenced from coding agents to fully agentic delivery.
• Act as the technical leader who sets priorities, removes cross-functional blockers and tracks value across 84.51°, KTD and Kroger technology teams.
• Deliver regular progress narratives to the senior leadership and the Capital Committee.
• Tie engineering outcomes to speed to market, released capacity and cost.
• Manage strategic vendor relationships across the AI tooling stack (for example Anthropic, Microsoft and GitHub, and Google) to shape roadmaps, enterprise terms and token economics.
• Serve as the internal and external voice of the transformation.

Reinvent the SDLC Operating Model

• Redesign each lifecycle stage so agents handle more of the work and the hand-offs between stages are automated, from requirements and design through testing, deployment and operations.
• Define how product, architecture, engineering, QA and SRE roles evolve in an agentic model, including the move from one-to-one assistance to engineers orchestrating multiple agents in parallel.
• Start with lighthouse teams, prove the model with measured results, then scale it through a repeatable playbook and reusable workflow patterns.
• Set the risk tiers that determine how much autonomy agents receive.

Coding Agent Rollout and Adoption

• Lead the enterprise rollout of Claude Code across all technical personas, building on general availability toward deep, daily use in every delivery team.
• Own the coding-agent portfolio, including GitHub Copilot, with clear guidance on when to use which tool, so that teams are not slowed by tool sprawl.
• Govern the enterprise-managed configuration: approved models, permissions, MCP servers, shared skills, hooks and data-handling settings.
• Embed forward deployed engineers with delivery teams to turn platform capability into real delivery outcomes and to create tight feedback loops.
• Grow a practitioner community of champions, office hours, show-and-tells and internal case studies that make the new way of working visibly better.
• Sequence follow-on Claude suite capabilities, such as Claude Cowork and Claude Design, without slowing core coding-agent enablement.

AI Developer Portal and Agentic Engineering Platform

• Stand up the AI Developer Portal as the single self-service front door for builders: model and agent access through AI Gateway, golden-path templates, an MCP server catalog, a prompt and skill marketplace, and documentation with built-in AI assistance.
• Set enterprise standards for agentic coding tools, agent harnesses and the managed agent platform.
• Make institutional knowledge, engineering standards and policies machine-readable (for example as repository instructions, skills and MCP-accessible knowledge) so agents apply them consistently.
• Scale CI/CD, test infrastructure and review capacity for a step-change in change volume, including deployment automation and standardized templates.
• Build production feedback loops that turn incidents and regressions into fixes.

AI Hub and Enterprise Enablement

• Partner with the AI Hub product team so the Hub clearly surfaces engineering journeys, approved tools, curated use cases and governance standards for associates across 84.51°, KTD and Kroger GO.
• Partner with AI Learning and Development to move engineers from AI awareness to applied, productive use through role-based training, guides and certification paths.
• Extend agentic workflows to product, analytics, design and other builders, with guardrails appropriate to each persona.

Quality, Security and Governance

• Keep people accountable for intent, architecture and high-risk approvals.
• Route changes by risk, with a lighter path for low-risk work and mandatory human review for high-risk and regulated code.
• Enforce policy as code through automated guardrails and approval gates, and run continuous evaluations in CI, including agent and model evaluations.
• Secure the agent supply chain: approved MCP servers, least-privilege tool access, secrets protection and prompt-injection defenses.
• Maintain a complete audit trail of agent-authored changes and clear separation of duties.
• Partner with Security, Legal, Privacy, Compliance and Audit, especially for pharmacy and health, payments and customer data.

Value, Cost and FinOps

• Manage agent compute and token spend with clear unit-cost visibility, such as cost per engineer and cost per merged change.
• Partner with Finance and FinOps on showback in the near term and a path to chargeback for consumption-based AI products such as Claude Code, AI Gateway, Databricks and LangSmith.
• Feed the enterprise Value Tracking Dashboard with credible, operational metrics rather than self-reported surveys.
• Use privacy-preserving, team-level telemetry, reviewed with HR and Legal, to measure organizational improvement rather than individual performance.

People, Culture and Adoption

• Build and lead a team of platform engineers, forward deployed engineers, evaluation engineers, product managers and enablement specialists.
• Upskill engineers to orchestrate agents, not just write code.
• Drive adoption by making this way of working genuinely better, not by mandate.
• Help redesign career paths, job architecture and hiring profiles for agentic engineering.

Measure and Communicate

• Own a single agentic engineering scorecard covering delivery speed, adoption, quality, security, cost and developer experience.
• Set baselines early so progress is always measured against where we started.
• Review the scorecard regularly with engineering leadership and the C-suite.
• Report transparently, sharing wins and risks alike.

What You’ll Bring

Required

• 12+ years in software engineering, with deep knowledge of production systems at scale.
• 5+ years leading leaders in multi-level engineering organizations.
• Experience delivering ML and AI applications to end users at scale.
• Proven, hands-on experience leading AI-enabled SDLC or developer-productivity transformation, Agent platform engineering across multiple teams.
• Strong command of agentic coding tools (for example Claude Code, GitHub Copilot and Codex), agent protocols such as MCP, evaluation methods, CI/CD, DevSecOps and observability.
• Experience running an internal developer platform or developer portal as a product.
• A track record of influencing and presenting to senior leadership.
• Excellent executive communication and storytelling.

Preferred

• Experience in a large, regulated enterprise such as retail, healthcare or pharmacy, or financial services.
• Platform engineering leadership.
• Enterprise rollout of AI tools to thousands of users, including enablement, licensing and usage governance.
• AI FinOps experience: token economics, showback and chargeback models.
• Change management at enterprise scale.
• Experience with Azure, GCP or AWS AI platforms and Databricks.
• External thought leadership on AI-enabled engineering.

How You Lead

• Thinks in systems and sees the whole lifecycle, not just the tools.
• Balances ambition with evidence and stays practical about what works.
• Obsesses over outcomes rather than activity.
• Builds coalitions across technology, business and control functions.
• Leads by example as a daily, hands-on user of the tools the organization is adopting.
• Communicates clearly with engineers and executives alike.

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