Lead AI/ML Engineer (P4363)

84.51° Overview

84.51° is a retail data science, insights and media company. We help The Kroger Co., consumer packaged goods companies, agencies, publishers and affiliates create more personalized and valuable experiences for shoppers across the path to purchase.

Powered by cutting-edge science, we utilize first-party retail data from more than 62 million U.S. households sourced through the Kroger Plus loyalty card program to fuel a more customer-centric journey using 84.51° Insights, 84.51° Loyalty Marketing and our retail media advertising solution, Kroger Precision Marketing.

84.51° follows a 5‑day in‑office work schedule to support collaboration, alignment, and team connection.

Join us at 84.51°!

QUALIFICATIONS, SKILLS, AND EXPERIENCE

Bachelor’s degree or higher in Computer Science, Computer Engineering, Software Engineering, or a related field.

· 4+ years of experience developing cloud-based software solutions and an understanding of design for scalability, performance, and reliability.

4+ years of experience applying programming languages, software design patterns, and modern engineering technologies to build production solutions.

2+ years of hands-on experience building and maintaining scalable AI-powered applications or agentic systems.

2+ years of experience with generative AI technologies preferred.

· 2+ years of experience in tech consulting, retail or related professional services preferred

Hands-on experience across the end-to-end software development lifecycle, using CI/CD, automated testing, and DevOps practices to develop, deploy, and maintain scalable solutions.

Experience delivering production-grade software services or agentic solutions that meet stakeholder needs.

Knowledge of generative AI application patterns, including retrieval-augmented generation, tool use, and agent orchestration, is highly preferred.

Experience working with a managed agent platform, such as LangSmith, to support agent deployment, tracing, evaluation, and lifecycle management.

Experience working with AI-ready data, including preparing, validating, and organizing governed data and metadata for reliable retrieval, context grounding, and agent use.

Experience working with a managed data platform, such as Databricks or Snowflake.

· Strong skills in Python

Strong data engineering and data modeling skills, including integrating data sources and building reliable data pipelines.

Experience writing agent evaluations is a plus.

Experience with prompt tuning is a plus.

Experience working with Model Context Protocol (MCP) servers is a plus.

$125,000 - $207,000 USD

Responsibilities

Serve as the technical engineering lead on complex, multi-month projects to build and maintain agentic AI systems across organizational domains.; Foster a collaborative and innovative team environment, encouraging professional growth and development among junior team members.; Leverage established software engineering patterns, frameworks, and tools to automate, deploy, and integrate generative AI and agentic solutions.; Develop tools, processes, and operational capabilities to monitor agent behavior, response quality, data accuracy, latency, and system reliability.; Partner with researchers, domain experts, and platform teams to optimize and scale agentic systems using software engineering, DevOps, and AI lifecycle best practices.; Package agent capabilities, tools, and supporting services as reusable APIs and components that can be integrated across the organization.; Build, steward, and maintain production-grade agentic systems and supporting backend services, including agent APIs, asynchronous jobs, conversation state, file storage, and enterprise integrations, with a focus on security, reliability, observability, scalability, and performance.; Evaluate emerging generative AI and agent frameworks, orchestration patterns, protocols, and tooling to identify opportunities for enterprise adoption and evolve existing systems.; Translate business requirements into consumable agent capabilities and services, balancing scale, risk, response quality, latency, and cost to maximize value.; Reduce time to delivery through automated build, test, and deployment pipelines, coordinated service releases, continuous evaluation, and user feedback and monitoring practices.; Responsible for code reviews and QA sign off; Maintain technical documentation, version control, deployment and rollback guidance, and knowledge-sharing practices to support collaboration and operational continuity.; Make time-sensitive decisions and solve urgent problems without escalation.

Requirements

Excellent communication skills, particularly on technical topics.; Strong time and project management skills, the ability to balance multiple, simultaneous work items and prioritize as necessary.; Kubernetes & Docker experience; CI/CD Pipeline experience, Github Actions a plus; Terraform experience