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84.51 Degrees

Data analytics for retail marketing decisions

Director, Agentic Engineering

Engineering LeadershipConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 12 years of software engineering experience, with deep knowledge of production systems at scale.
  • At least 5 years leading leaders in multi-level engineering organizations.
  • Experience delivering machine learning and artificial intelligence applications to end users at scale.
  • Hands-on experience leading AI-enabled software development lifecycle or developer-productivity transformation and agent platform engineering across multiple teams.
  • Strong command of agentic coding tools such as Claude Code, GitHub Copilot and Codex; agent protocols such as Model Context Protocol; evaluation methods; continuous integration and continuous delivery; 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.
  • Ability to think in systems and understand the full lifecycle, not just the tools.
  • Ability to balance ambition with evidence and practical judgment.
  • Focus on outcomes rather than activity.
  • Ability to build coalitions across technology, business and control functions.
  • Leads by example as a daily, hands-on user of the tools the organization is adopting.
  • Ability to communicate clearly with engineers and executives.

Responsibilities

  • Build the multi-year agentic engineering strategy and investment case in partnership with the C-suite, sequencing from coding agents to fully agentic delivery.
  • Set priorities, remove cross-functional blockers and track value across 84.51°, KTD and Kroger technology teams.
  • Deliver regular progress narratives to 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, including Anthropic, Microsoft, GitHub and Google, to shape roadmaps, enterprise terms and token economics.
  • Serve as the internal and external voice of the transformation.
  • Redesign each software development lifecycle stage so agents handle more work and hand-offs are automated, from requirements and design through testing, deployment and operations.
  • Define how product, architecture, engineering, quality assurance and site reliability engineering roles evolve in an agentic model, including engineers orchestrating multiple agents in parallel.
  • Pilot the agentic operating model with lighthouse teams, demonstrate measured results, and scale it through a repeatable playbook and reusable workflow patterns.
  • Set risk tiers that determine how much autonomy agents receive.
  • Lead enterprise rollout of Claude Code across technical personas, advancing from general availability toward daily use in every delivery team.
  • Own the coding-agent portfolio, including GitHub Copilot, and provide guidance on tool selection to prevent tool sprawl.
  • Govern managed configurations, including approved models, permissions, Model Context Protocol servers, shared skills, hooks and data-handling settings.
  • Embed forward deployed engineers with delivery teams to translate platform capability into delivery outcomes and create feedback loops.
  • Develop a practitioner community through champions, office hours, show-and-tells and internal case studies.
  • Sequence follow-on Claude suite capabilities, such as Claude Cowork and Claude Design, without slowing core coding-agent enablement.
  • Establish the AI Developer Portal as a self-service front door for builders, providing model and agent access through AI Gateway, golden-path templates, a Model Context Protocol 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 so agents apply them consistently.
  • Scale continuous integration and continuous delivery, test infrastructure and review capacity for increased change volume, including deployment automation and standardized templates.
  • Build production feedback loops that turn incidents and regressions into fixes.
  • Partner with the AI Hub product team to surface 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 use through role-based training, guides and certification paths.
  • Extend agentic workflows to product, analytics, design and other builders, with appropriate guardrails for each persona.
  • Keep people accountable for intent, architecture and high-risk approvals.
  • Route changes by risk, using 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 continuous integration, including agent and model evaluations.
  • Secure the agent supply chain through approved Model Context Protocol 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.
  • 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 near-term showback 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.
  • Build and lead a team of platform engineers, forward deployed engineers, evaluation engineers, product managers and enablement specialists.
  • Upskill engineers to orchestrate agents rather than only write code.
  • Drive adoption by making the new way of working better rather than relying on mandate.
  • Help redesign career paths, job architecture and hiring profiles for agentic engineering.
  • Own a single agentic engineering scorecard covering delivery speed, adoption, quality, security, cost and developer experience.
  • Set baselines early so progress is measured against the starting point.
  • Review the scorecard regularly with engineering leadership and the C-suite.
  • Report transparently, sharing wins and risks.

Desired Qualifications

  • 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, including token economics and showback and chargeback models.
  • Change management at enterprise scale.
  • Experience with Azure, Google Cloud Platform or Amazon Web Services AI platforms and Databricks.
  • External thought leadership on AI-enabled engineering.

About the Company

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84.51 Degrees

Data analytics for retail marketing decisions

84.51 Degrees analyzes large volumes of consumer data to help retailers and consumer goods companies understand shopper behavior and tailor marketing strategies. Its main product is data analytics and predictive analytics tools that uncover consumer insights, trends, and preferences across generations. These insights are delivered to B2B clients via subscriptions, project-based work, or performance-based pricing, enabling clients to make data-driven decisions and optimize customer engagement across the purchasing journey. The company differentiates itself through a strong emphasis on culture and inclusive workplace values, which it views as important for driving collaboration and innovation. Overall, the goal is to help clients stay relevant and competitive by translating data into actionable marketing and merchandising strategies.

Company size
1,001-5,000
Company stage
M&A
Total funding
$5.5M
Headquarters
Cincinnati, OH
Founded
2015

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