Senior Data Scientist Consultant, Managed Agent Platform (MAP) – Agent Platform Team P2392 

Summary 

The Agent Platform Team, within AI Enablement at 84.51°, owns and manages the core platforms that power data science and AI across the organization. This role sits on the MAP (Managed Agent Platform) squad and is focused on helping stand up a new, centralized platform that simplifies and accelerates the design, development, deployment, and monitoring of AI agents across the organization. 

The MAP is being built in response to growing demand for a flexible, no-code/low-code solution that enables both technical and non-technical users to build and deploy agents without deep engineering overhead. The platform is early-stage — the team is actively evaluating different platforms and tooling, defining workflows, and establishing standards. You will be joining at the ground floor and helping shape what this platform becomes. 

You will be the bridge between the engineers building/maintaining the MAP and the data scientists and agent builders who will use it. You are not building agents for end-clients. Instead, you ensure the platform works for the people on it — that capabilities are tested, documented, and adopted smoothly, and that practitioner needs make it back to the engineering team in a way that shapes the roadmap. 

You’ll bring hands-on familiarity with the open-source agent ecosystem — including frameworks like LangChain, LangGraph, Google ADK, and OpenAI Agents SDK — and apply that knowledge to evaluate platform capabilities, inform design decisions, and help practitioners understand what’s possible. As the platform matures, you’ll help onboard users, develop best practices, and drive adoption across the organization. 

This is a small, engineer-heavy squad that is standing something up from scratch. Everyone pitches in on a bit of everything. Priorities will shift frequently as the platform takes shape. Success in this role is less about fixed technical expertise and more about comfort with ambiguity, good judgment about where to spend your time, and the ability to influence without direct authority. 

Responsibilities 

• Voice of the practitioner: Serve as the primary liaison between data scientists and agent builders and the MAP engineering team. Gather requirements, surface pain points, and translate practitioner needs into actionable feedback that shapes platform decisions, standards, and the roadmap. 

• Platform evaluation & definition: Help evaluate and select platform tooling and contribute to defining core platform capabilities — including agent building workflows, orchestration patterns, tool/API connectivity, observability and tracing, evaluation frameworks, and governance controls. Test and document findings, recommended use cases, limitations, and rollout considerations. 

• Onboarding & enablement: As the platform matures, write guides, maintain documentation, lead training sessions, and run office hours to help data scientists and agent builders adopt the MAP. Serve as a first line of support when teams hit roadblocks. 

• Adoption & communication: Evangelize the MAP to increase awareness and drive adoption across the organization. Produce clear communications and materials for both technical and non-technical audiences. 

• Cross-functional collaboration: Work closely with engineers, the solution architect, and product lead on the squad to ensure platform capabilities have well-defined use cases and that guidelines and best practices are established. Act as an agent-building ambassador to cross-functional teams, explaining recommended workflows and the reasoning behind platform choices. 

• Governance & compliance support: Help validate that platform capabilities meet enterprise requirements around security, compliance, observability, scalability, and data governance. 

Qualifications & Experience 

• Bachelor’s degree in mathematics, statistics, computer science, economics, or a related discipline. 

• 2–4 years of experience in data science, AI/ML development, or applied analytics. 

• 2–4 years of experience in tech consulting, retail analytics, or related professional services. 

• Hands-on experience with Python and SQL. 

• Hands-on experience with one or more agent frameworks (e.g., LangChain, LangGraph, Google ADK, OpenAI Agents SDK, CrewAI, AutoGen) is required. 

• Experience gathering user needs, documenting requirements, and driving issues to resolution with technical teams. 

• Experience partnering with software engineering and/or product management teams. 

• Familiarity with LLMs, prompt engineering, and agentic AI concepts. 

• Exposure to agent platforms is a strong plus. 

Skills 

• Strong written and verbal communication, including the ability to explain technical concepts clearly to non-technical audiences and produce high-quality documentation. 

• Ability to influence across teams and seniority levels without direct authority. 

• Comfort operating in ambiguity — this platform is early-stage, and priorities will shift frequently as the team learns what works. 

• Strong project and time management skills; able to manage multiple priorities with minimal oversight. 

• Deep curiosity about the agentic AI ecosystem and a genuine desire to become a subject matter expert on agent orchestration, evaluation, observability, and deployment patterns. 

• Comfort working on a small, engineer-heavy team where you are often the only non-engineer voice in the room. 

#LI-SSS

Lead Data Scientist, Relevancy Sciences Data Science & Research – Personalization & Loyalty Strategy (P3764)

Relevancy Sciences Team is responsible for powering relevant, personalized, and scalable customer experiences across Kroger’s e-commerce ecosystem. We build and evolve the science behind search and recommendations that serve millions of customers and support high-scale digital experiences.

We are seeking a Lead Data Scientist to provide technical leadership across search and recommender systems, with a strong focus on modern model architectures, and production-ready machine learning. This role is ideal for someone who combines depth in applied machine learning with strong systems thinking, cross-functional influence, and contributes towards agentic capabilities.

What does the role entail? (Responsibilities)

Technical Leadership, Ownership, and Influence. Own and drive technical initiatives across search & recommender systems. Define and evolve the science strategy for improving content discovery, relevance, personalization, and decision support across digital experiences. Identify high-impact opportunities, make clear technical tradeoffs, and guide the team towards scalable, practical solutions. Rapidly prototype and validate new ideas to accelerate adoption and demonstrate measurable value.

Develop innovative search & recommender systems. Design and build ML solutions tailored to the unique needs of grocery retail domain. Lead the development of systems that improve product discovery and personalization across customer journeys. Bring strong technical and thought leadership on next generation personalization, including the use of Generative AI and agent-based approaches.

Evaluate and improve ML performance. Establish rigorous evaluation methodologies to assess the performance of ML systems across key metrics. Define robust online evaluation frameworks, and guide experimentation strategies that connect model improvements to customer and business outcomes.

Model serving and deployment. Partner closely with Engineering to build and deploy production-ready ML systems. Influence design decisions related to real-time inference, feature access, system integration, monitoring, and reliability. Ensure solutions meet latency, scalability, and operational requirements. Contribute to the evolution of serving and deployment strategies.

Cross-functional leadership. Work closely with Product, Engineering, and business stakeholders to translate needs into clear problem statements, hypotheses, and execution plans. Drive alignment across teams and influence decisions through clear communication of tradeoffs, risks, and expected outcomes.

Mentoring and knowledge sharing. Mentor data scientists and lead technical reviews to improve model quality, experimentation rigor, and systems thinking. Promote best practices in reproducibility and evaluation. Contribute to building a strong, learning-oriented team culture.

What skills and experience do you need? (Requirements)

• Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• 6+ years of experience applying machine learning to real-world problems with strong experience in search and recommender systems. Strong understanding of approaches such as embeddings and multi-stage decision systems.
• Strong proficiency in Python and SQL, with experience working on large-scale data using tools such as Spark.
• Experience with modern machine learning and deep learning frameworks such as PyTorch or TensorFlow.
• Strong foundation in statistics, experimentation, and data analysis, including design of experiments and A/B testing.
• Familiarity with large language models, foundation models, and emerging AI capabilities, including where they are applicable and where they are not.
• Experience evaluating or prototyping GenAI-based solutions is preferred.
• Experience partnering with engineering teams to deploy and maintain machine learning systems in production.
• Understanding of real-time systems, model serving, feature pipelines, and monitoring.
• Ability to make practical tradeoffs between model complexity, performance, latency, and scalability.
• Demonstrated ability to lead technical work across projects and influence direction across data science, engineering, and product teams.
• Experience mentoring or guiding other data scientists and contributing to a strong technical culture.
• Experience working with cloud platforms such as GCP or Azure.
• Experience in retail, e-commerce, or high-scale consumer domains is a plus.

Why join our team? (Rewards)

Impact millions of people. As a member of our data science team, you will have the opportunity to make a tangible difference in the lives of millions of customers by delivering relevant and personalized recommendations that enhance their grocery shopping experience. Your work will directly contribute to increasing customer satisfaction and loyalty, driving business outcomes for our company.

Continuous learning and development. Challenge yourself. We are committed to fostering a culture of continuous learning and development. You will have access to resources and support for expanding your knowledge and skills in cutting-edge technologies, including recommender systems, machine learning and artificial intelligence. Our team encourages exploration and experimentation, providing opportunities to stay at the forefront of industry advancements.

Work on new developments in recommender systems. Join a team at the forefront of innovation in recommender systems and AI. You will have the chance to contribute to pushing the boundaries of what’s possible in personalized recommendation technology. You will have the chance to work on exciting projects that leverage the latest developments in deep learning architectures and data science methodologies.

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Senior Data Scientist Consultant, SPI – Agent Platform Team

Summary

The Agent Platform Team, within AI Enablement at 84.51°, owns and manages the core platforms that power data science and AI across the organization. This role sits on the SPI (Science Platform Innovation) squad and is focused specifically on the Databricks environment and its evolving AI/ML capabilities.

You will be the bridge between the engineers building and maintaining our Databricks platform and the data scientists who use it every day. You are not building models for end-clients. Instead, you ensure the platform behaves as expected for the people on it—that new capabilities are tested, documented, and adopted smoothly, and that practitioner pain points make it back to the platform team in a way that shapes the roadmap.

You’ll build deep familiarity with Databricks’ infrastructure and AI powered capabilities —including Genie Code, Genie spaces, Agent Bricks, MLflow, and other features as they emerge—as well as the Unity Catalog governance layer that underpins them. You’ll test and evaluate new Databricks features and relevant third-party integrations from the perspective of an end-user, document your findings, and help the team decide what to adopt, how to roll it out, and what to skip.

This is a small, highly collaborative squad. Everyone pitches in on a bit of everything. Priorities shift frequently as the Databricks roadmap evolves. Success in this role is less about fixed technical expertise and more about comfort with ambiguity, good judgment about where to spend your time, and the ability to influence without direct authority.

Responsibilities

• Voice of the practitioner: Serve as the primary liaison between data scientists using Databricks and the platform engineering team. Gather requirements, surface pain points, and translate practitioner needs into actionable feedback that shapes platform decisions and roadmaps.
• Technology evaluation: Lead the testing and evaluation of new Databricks capabilities (e.g., Mosaic AI features, Model Serving enhancements, Genie, new Unity Catalog functionality) and relevant third-party tools. Document findings, recommended use cases, limitations, and rollout considerations. Over time, this may evolve from hands-on beta testing toward designing and coordinating evaluation efforts across groups of practitioners.
• Onboarding & enablement: Write guides, maintain documentation, lead training sessions, and run office hours to help data scientists adopt new platform capabilities. Partner with Learning & Development where appropriate. Serve as a first line of support when teams hit roadblocks.
• Adoption & communication: Evangelize Databricks capabilities to increase awareness and drive adoption. Produce clear release notes, communications, and materials for both technical and non-technical audiences.
• Cross-functional collaboration: Work closely with the solution architect, cloud engineer, and product senior on the squad to ensure proposed technologies have well-defined use cases and that guidelines and best practices are established. Act as a data science ambassador to cross-functional teams, explaining recommended workflows and the reasoning behind platform choices.
• Governance & compliance support: Help validate that new platform capabilities meet enterprise requirements around security, compliance, observability, and data governance (e.g., Unity Catalog lineage, Azure connectivity).

Qualifications & Experience

• Bachelor’s degree in mathematics, statistics, computer science, economics, or a related discipline.
• 2–4 years of experience in data science, AI/ML development, or applied analytics.
• 2–4 years of experience in tech consulting, retail analytics, or related professional services.
• Hands-on experience with Python, SQL, PySpark and Databricks
• Experience gathering user needs, documenting requirements, and driving issues to resolution with technical teams.
• Experience partnering with software engineering and/or product management teams.
• Familiarity with LLMs, prompt engineering, or agentic AI concepts is a strong plus.
• Experience with the Databricks ecosystem (Unity Catalog, MLflow) is a strong plus.
• Exposure to agent frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen) is a plus.

Skills

• Strong written and verbal communication, including the ability to explain technical concepts clearly to non-technical audiences and produce high-quality documentation.
• Ability to influence across teams and seniority levels without direct authority.
• Comfort operating in ambiguity—priorities shift frequently, and you need good instincts about where to focus.
• Strong project and time management skills; able to manage multiple priorities with minimal oversight.
• Curiosity and enthusiasm for emerging AI technologies and a genuine desire to become a subject matter expert on the Databricks platform and its AI capabilities.

#LI-SSS

84.51° is a retail data science, insights, and media company that helps brands grow by delivering smarter, more accountable marketing powered by Kroger’s first-party data. Kroger Precision Marketing (KPM) is our commercial arm, enabling brands to connect with customers through insights, incentives, and media.

Role Summary

Kroger Precision Marketing (KPM) recently launched a new AI Platform team to help product teams bring safe, scalable AI to market across our brand-facing platforms. We do that by (1) consulting on standards and guardrails; (2) accelerating reusable tools and early features, then transitioning long-term ownership to product teams; and (3) operationalizing a repeatable way to ship, monitor, and improve AI at scale.

As a Product Manager on this team, you will work alongside other PMs and a Product Director who sets the overall vision and strategy. You will own end-to-end initiatives that span multiple projects and features, from discovery through delivery and adoption, and you will be accountable for the business outcomes and KPIs those initiatives drive. You will also build and evolve reusable AI tooling for other teams, and consult with those teams to align strategies, reduce duplication, and ensure we ship AI consistently and safely across KPM’s brand-facing platforms.

Key Responsibilities

• Build the game plan: translate the AI portfolio vision into a clear roadmap for your initiatives
• Pick the right bets: focus the roadmap on the highest-impact work by weighing impact, effort, and risk, and deprioritizing the rest
• Stay ahead of the market: stay current on AI trends, vendors, and competitor moves, and use those insights to inform recommendations and influence our strategy
• Have a point of view, stay curious: form a clear perspective, seek input early, and adjust quickly when evidence points to a better approach
• Get close to users: partner with Experience Design to run discovery and define what we will build, why it matters, and why now
• Ship with momentum: drive delivery with Agile Delivery, Data Science, and Engineering by managing scope, dependencies, risks, tradeoffs, and timelines
• Prove and improve: measure outcomes against business KPIs, collect user and stakeholder feedback, and iterate based on real signal (qualitative and quantitative)
• Make it stick: support rollout and adoption, then hand off with clear enablement and accountability
• Scale the capabilities: own reusable AI capabilities, including skills, patterns, and tooling, that help teams across the company build smarter and faster
• Tell the story: communicate progress, tradeoffs, and decisions with clarity, and raise risks early with context and options
• Be the go-to partner: earn trust through strong follow-through, clear communication, and reliable decision-making, while balancing confidence with humility

Minimum Qualifications

• Product management: 3–5 years of product management experience
• AI/ML delivery: familiarity with the AI/ML product lifecycle, including data readiness, model development, deployment, monitoring, and governance
• GenAI fundamentals: working knowledge of prompting, retrieval-augmented generation (RAG), agent patterns and skills, and quality and safety evaluation
• Roadmap: ability to set priorities and drive execution independently
• Decision-making: ability to make calls, clarify tradeoffs, and create clarity in ambiguity
• Delivery: experience partnering with Experience Design, Data Science, Engineering, and Agile Delivery
• Communication: ability to translate between technical and business teams, manage stakeholders, and raise risks early with context and options
• Metrics: ability to define success, measure outcomes, and iterate based on real signal
• Leadership: a humble, direct leadership style, comfortable saying “I don’t know,” and confident leading toward an answer
• Collaboration: ability to move fast within security, governance, and complex stakeholder environments

Preferred Qualifications

• B2B platform: experience building products for large, diverse internal or external user bases
• Industry: familiarity with retail analytics, retail media, adtech, and data-driven marketing platforms
• Technical fluency: background in engineering, data science, analytics, or a highly technical product area, with the ability to go deep on architecture and delivery tradeoffs
• Experimentation and measurement: product analytics experience defining success metrics, selecting the right measures for the job (for example, using Google’s HEART framework), and running experiments to quantify user and business impact

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Lead Technical Product Manager(G3) 

LOCATION: Chicago/Cincinnati 

COMPANY OVERVIEW: 

84.51° knows customers, and we know how to connect you.  

Using a sophisticated, proprietary suite of tools and technology, we turn customer data into actionable knowledge. With unparalleled customer data and predictive analytics capabilities, we deliver personalized marketing strategies and ensure the best experience for customers of Kroger and more than 300 consumer-packaged-goods companies. We put the customer at the center of everything we do, resulting in a more dynamic, informed and personal approach to driving customer loyalty. 

SUMMARY:  

The AI Enablement team builds foundational AI capabilities that transform Kroger and 84.51, including better solutions and outcomes for our shoppers and our associates. AI Gateway is a cross-functional team within AI Enablement responsible for the primary AI model and API infrastructure that powers these solutions and democratizes access to AI across the org. As the Product Manager for this team, you will work closely with the Director of Product, Tech Leads for the AI Gateway, and cross-functional design and development teams. 

The LEAD PRODUCT MANAGER is the chief navigator, business champion, and team linguist that is accountable for setting team priorities and maximizing the value of the product results from the work of the development team. You own the details of the business and technical value delivered at the team level. You translate business requirements into product requirements to ensure that the product delivers on user needs. You own the measurement of the value delivered and other metrics for your product. This role requires excellent organizational and communication skills, technical acumen, and artificial intelligence domain knowledge. 

RESPONSIBILITIES:  

• Drive business value for the team by prioritizing and ordering product backlog items based on the product roadmap, inclusive of product, engineering, UX and data science roadmaps. 
• Experienced in managing multiple teams’ product backlogs in parallel and ensuring they’re transparent, visible and clearly communicated.
• Partner with cross-functional engineering, UX and data science peers to translate business desires/requirements into effective features and stories that are ready for development.
• Outlines clear acceptance criteria, to clearly communicate the definition of “done”.
• Partner with external contractors or technical support to ensure the delivery of the scope of work, within the agreed timeline, and in accordance to the stated acceptance criteria.
• Deep understanding of the business domain, stakeholders, business processes and constraints in which your product(s) play 
• Deep understanding of customers/users’ problems, how they think and operate and the platforms/technology they use. Ability to become familiar with new technology, business domains, and/or analytical practices that impact your product(s). 
• Understand and communicate organization objectives, strategies and solution needs to the team to facilitate results (where collaborative/as colleague)
• Manage and communicate dependencies and risks.
• Stakeholder management – experienced in building and managing internal and external stakeholder relationships to influence and drive change.
• Experienced level knowledge in the development and use of product data to influence product decisions/roadmap – both quantitative and qualitative.
• Clearly communicate the value of the work being delivered

QUALIFICATIONS, SKILLS, AND EXPERIENCE:  

• Professional work experience in artificial intelligence / Generative AI applications.
• Experience managing and/or developing an API-based product or developer tooling.
• Experience managing and/or developing cloud-based solutions in Azure or Google Cloud.
• Experience scaling apps or systems to meet high-availability and resiliency requirements for critical internal and customer facing solutions.
  • Experience managing and/or working on a team that deploys AI/ML models in cloud-based settings.
  • Technical acumen – possess the foundational technical knowledge needed to communicate with the technical team, understand blockers/trade-offs, and incorporate technical needs in the roadmap.
  • Experience navigating corporate governance processes including Security, Cloud, Privacy, Data Sharing, and Legal.
• Skilled in managing cross-functional software development teams in an agile environment
• Skilled in managing business priorities against design/user needs while delivering against product roadmaps
• Skilled in managing end-to-end software product development.
• Stakeholder management skills – experienced in building and managing stakeholder relationships to influence and drive change.
• Problem solving, critical thinking and analytical skills
• Planning, organizational and time management skills
• Comfort with ambiguity – remains confident, positive and resourceful in the face of ambiguity and unexpected results; recovers quickly from setbacks.
• Exhibits qualities of and have proven to be a great teammate
• Verbal and written communication skills, ability to translate between business and technical
• 4-6 years relevant experience
• Education: Bachelor’s degree or equivalent experience

ADDITIONAL SKILLS OF INTEREST (NOT REQUIRED) 

• Prior experience as a software engineer or Technical Product Manager – many of our customers are software engineers and data scientists.
• Experience leveraging various Generative AI chat tools (ie M365 Copilot, ChatGPT, Gemini, Claude) to simplify your life, work, or otherwise automate processes and accomplish previously impossible things. 
• Experience developing AI Agents using an agent builder or agent framework.
• Experience developing or using MCP servers.
• Experience using coding agents (ie Claude Code, Codex, Gemini CLI, Github Copilot).

Director, Software Engineering – Agentic Foundations (P4474)

PREFERRED LOCATION: Cincinnati / Chicago

SUMMARY:

The AI Enablement team at 84.51° is seeking a Director of Software Engineering for its Agentic Foundations team. This team is responsible for designing, developing, and maintaining the agentic backbone of the Kroger enterprise, including platforms, patterns, and tools to support the best practice development of AI agents.

The Director of Engineering requires a unique mix of software engineering and AI skills necessary to lead the technical design of platforms, patterns, and tools to support the enterprise agentic ecosystem. This role has a focus on providing the technical leadership needed for engineering and data science development teams implementing solutions like agent/MCP/scorer/prompt registries, agent-related tools to aid in the development and monitoring of best practice agents and tools, and managing low-code and no-code agent platforms that integrate with agentic support.

RESPONSIBILITIES:

• Partner with the Director of Product Management for Agentic Foundations to jointly lead the space for the Kroger enterprise
• Lead and manage a team of engineers, guiding them in the design and development of their product/platform areas and in the management of their own reports
• Lead and manage hiring efforts for engineers on the Agentic Foundations team, including finding interview panelists, writing interview guides and questions, and assisting with coding screening questions
• Own and manage offshore reliability engineering teams
• Provide in-depth technical leadership in the scoping and implementation of enterprise-grade solutions to support agents across the Kroger enterprise
• Provide operational leadership across agentic platforms, ensuring enterprise-grade reliability, observability, and incident management
• Maintain an up-to-date expertise on agentic workflows, frameworks, platforms, and best practices to identify and inform new capabilities for implementation
• Foster a collaborative and innovative team environment, encouraging professional growth and development among team members
• Reduce the development time of the team by leveraging agentic SDLC tooling provided by the enterprise
• Lead the abstraction of tooling and services to user-friendly interfaces like APIs, Python packages, or UIs
• Understand business requirements and trade-off scale, risk, and accuracy to maximize value and translate research into consumable products or services
• Guide the development teams to follow software development standards, including version control, documentation, testing, and support
• Make time-sensitive decisions and solve urgent problems without escalation

QUALIFICATIONS, SKILLS, AND EXPERIENCE:

• 7+ years professional experience with cloud-based software architecture focused on scalability, performance, and reliability
• 7+ years professional experience with end-to-end SDLC leveraging CI/CD and AIOps/MLOps, including experience with agentic SDLC tooling
• 7+ years professional experience with large-scale AI/ML systems, including 2+ years with GenAI and deep learning methods
• Professional experience with production-grade agentic systems, including concepts like design and development, evals, deployment, and monitoring
• Professional experience with AI/ML frameworks and tooling (PyTorch, OpenAI SDK, LangChain, autogen)
• Professional experience with AIOps/MLOps tooling and concepts (MLflow, Model Registry, Model Monitoring)
• Professional experience with scaling and deploying AI/ML systems using tooling in Microsoft Azure and Google Cloud, including CI/CD, containerization, orchestration, and API development (in particular FastAPI)
• Professional experience with Kubernetes (AKS) and Helm
• Professional experience with Python
• Professional experience with infrastructure-as-code using Terraform
• Professional experience leading technical teams and consulting with customers, including managing senior engineers, communicating complex technical topics, and driving outcomes in ambiguous environments
• Skilled in partnering with product management peers to jointly define product and team direction and priorities
• Education: Bachelor’s degree or higher in Software Engineering, Computer Science, Machine Learning, or a related field

#LI-SSS

#LI-Remote

Lead Technical Product Manager – Agent Platform 

LOCATION: Cincinnati / Chicago (P4476) 

SUMMARY:  

The AI Enablement team at 84.51° is seeking a Lead Product Manager for its Agent Platform team. This team is responsible for architecting, implementing, maintaining, and supporting the use of the agentic foundation of the Kroger enterprise. 

The Lead Product Manager will discover, prioritize, plan the implementation of the agent platform – this could include: a control layer consisting of agent, MCP, eval, and prompt registries; evaluation and monitoring tools; and pro-code, low-code, and no-code development capabilities. The role will partner closely with engineering and data science leads to shape the roadmap, translate business and technical needs into clear requirements, and ensure the delivery of high-value, enterprise-grade capabilities that reduce time-to-value, improve quality and reliability, and promote safe, compliant use of agentic systems. 
 

RESPONSIBILITIES: 

• Own product discovery and roadmap for the managed agent platform 
• Maintain a current roadmap and communicate trade-offs, dependencies, and timelines to stakeholders and delivery teams 

• Translate business goals, user needs, and technical constraints into product requirements and acceptance criteria with a clear definition of done 

• Partner with the Agentic Foundations leadership, as well as engineering and data science leads, to plan quarters/cycles/sprints and ensure efficient, predictable delivery 

• Define and evolve core agentic platform capabilities, including: 

• Registries (agent/MCP/scorer/prompt), templates, and pattern libraries 

• Agent development and monitoring tools, including evaluation frameworks and observability 

• Integration with data and API layers and pro-code agent frameworks 

• Integration with enterprise AIOps/MLOps, CI/CD, and governance workflows 

• Facilitate user research and testing to understand developer and business user workflows; validate usability, safety, reliability, and performance 

• Analyze product usage and operational data to inform prioritization, measure value, and guide iteration 

• Drive adoption across customer teams through documentation, enablement, demos, and feedback loops; remove friction points and champion best practice 

• Ensure alignment with enterprise strategies for scale, reliability, safety, compliance, and cost-efficiency 

• Manage progress toward objectives; proactively surface risks, blocks, and cross-team dependencies; coordinate mitigation plans 

• Develop strong internal and external stakeholder relationships; influence and drive change in ambiguous, evolving environments 

• Communicate realized value and outcomes, including usage, adoption, productivity improvements, and risk reduction 

QUALIFICATIONS, SKILLS, AND EXPERIENCE:  

• Professional experience in leading enterprise AI, ML, or data platforms 

• Professional experience in a product/software development team  

• Professional experience in managing technical and business stakeholders  

• Professional experience in ambiguous environments  

• Skilled in a general technical acumen, able to efficiently converse with software engineers and AI engineers and understand their development processes  

• Skilled in managing cross-functional technical development teams in an agile environment  

• Skilled in managing business priorities against design/user needs while delivering against product roadmaps  

• Skilled in managing end-to-end software product development  

• Skilled in problem solving, analytic thinking, and creativity  

• Skilled in planning, organization, and verbal/written business communication  

• Skilled in maintaining a positive leader persona in ambiguous and changing environments  

• Skilled in quickly flexing between technical and business stakeholder conversations, adapting to a variety of audiences  

• Demonstrated excellence as a teammate and leader  

• 4-6 years relevant experience  

• Education: Bachelor’s degree or higher in Software Engineering, Computer Science, Machine Learning, or a related field 

#LI-SSS

Lead Software Engineer – Agentic Foundations (P4475, P4477)

LOCATION: Cincinnati / Chicago

SUMMARY:

The AI Enablement team at 84.51° is seeking a Lead Software Engineer for its Agentic Foundations team. This team is responsible for designing, developing, and maintaining the agentic backbone of the Kroger enterprise, including platforms, patterns, and tools to support the best practice development of AI agents.

The Lead Software Engineer will provide hands-on technical leadership across architecture, implementation, and operations of core agentic capabilities, including agent/MCP/scorer/prompt registries, evaluation and monitoring tooling, and low-code/no-code agent platforms that abstract complexity for developers and business users. You will partner closely with product management, data science, and engineering peers and leaders to deliver enterprise-grade capabilities that accelerate time-to-value, improve quality and reliability, and promote compliant, best-practice use of agentic systems.

RESPONSIBILITIES:

• Lead the design, development, and operation of core agentic platforms, services, and tooling used across the Kroger enterprise
• Own technical outcomes for a more specific product area, such as registries (agent/MCP/scorer/prompt), evaluation frameworks, observability/monitoring, SDKs/APIs/UIs, and low-code/no-code agent platforms
• Provide in-depth technical leadership to scope and implement scalable, secure, and reliable solutions;
• Uphold standards for architecture, code quality, testing, and documentation
• Partner with the Agentic Foundations leadership, as well as engineering and data science leads, to plan quarters/cycles/sprints and ensure efficient, predictable delivery
• Work with offshore reliability engineering teams to ensure SLAs are upheld
• Occasional participation in level 2 on-call support for production systems
• Abstract complex capabilities into developer-friendly interfaces (APIs, Python packages, CLIs, and UIs) to reduce friction and accelerate adoption
• Drive operational excellence across platforms, including incident management, on-call support, capacity planning, and cost optimization
• Mentor and guide engineers through code reviews, architecture discussions, and pairing; contribute to team growth, onboarding, and enablement

QUALIFICATIONS, SKILLS, AND EXPERIENCE:

• Professional experience with cloud-based software architecture focused on scalability, performance, and reliability
• Professional experience with end-to-end SDLC leveraging CI/CD and AIOps/MLOps, including experience with agentic SDLC tooling
• Professional experience with large-scale AI/ML systems, including 1+ years with GenAI and deep learning methods
• Professional experience with AI/ML frameworks and tooling (PyTorch, OpenAI SDK, LangChain, autogen)
• Professional experience with AIOps/MLOps tooling and concepts (MLflow, Model Registry, Model Monitoring)
• Professional experience with scaling and deploying AI/ML systems using tooling in Microsoft Azure and Google Cloud, including CI/CD, containerization, orchestration, and API development (in particular FastAPI)
• Professional experience with Kubernetes (AKS) and Helm
• Professional experience with Python
• Professional experience in ambiguous environments
• Skilled in problem solving, analytic thinking, and creativity
• Skilled in planning, organization, and verbal/written business communication
• Skilled in maintaining a positive leader persona in ambiguous and changing environments
• Demonstrated excellence as a teammate and leader
• 4-6 years relevant experience
• Education: Bachelor’s degree or higher in Software Engineering, Computer Science, Machine Learning, or a related field

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