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. 

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Callibrity is a developer owned and managed custom software development consulting company that is dedicated to creating quality software using modern technologies and adding unquestionable business value to companies across multiple industries and verticals. We are problem solvers...people who like a challenge and enjoy working with modern tech stacks. We offer an incredibly collaborative culture and enjoy solving complex problems with our clients. If you are looking for a company where you can grow in your career and that values people, transparency, and flexibility we would like to talk to you.

As a Senior Software Engineer, you are a leader by example, you are a subject matter expert, and you help lead strategic technical direction. You will get to interact with key client stakeholders on a regular basis and use your experience to prove business ROI through innovative greenfield technology solutions. Software Engineer typically have longer tenures and higher retention rates with Callibrity than other companies because the work is challenging and rewarding, we have a strong engineering culture, and we invest in the best people.

Responsibilities:

• Are trusted advisors to our clients and seen as thought leaders in the tech industry
• Are keenly aware of big picture business problems and goals and capable of a leadership role
• Mentor, train, and upskill your Callibrity peers and clients’ teams
• Can solve a wide variety of problems with the appropriate solution drawn from a large subset
• Communicate complex ideas effectively, both verbally and in writing
• Proactively identify problems, challenges, and issues and recommend the best possible solution
• Demonstrate mastery and cable to teach software development best practices (Agile, DevOps, Test Automation, CI/CD, TDD) to others
• Stay ahead of the curve by exploring new technologies including AI, Machine Learning, Cloud, and IoT
• Thrive in an environment where there is close interaction among different roles and teams
• Are admired by the client as someone they can count on, regardless of title or seniority

Requirements:

• 5+ years of experience with web application development and/or SaaS platforms.
• Extensive senior-level development experience with object-oriented languages in Python, FastAPI/Flask, or Java
• Proven experience leading the technical delivery of complex and innovative business functionality.
• Strong understanding of software engineering principles such as design patterns, algorithms, and data structures, and demonstrable application of each in recent projects.
• Cloud experience with AWS, Azure, and/or Google Cloud Platform preferred
• Experience with Docker, Kubernetes, and/or developing applications for a major public cloud provider is a proactive owner who takes accountability for code from development through production.
• Strong knowledge of performance engineering and performance testing.
• Experience with integration technologies (RESTful APIs, messaging systems, etc.)
• Relevant professional experience in Information Technology and/or Consulting
• Four-year degree or successful completion of respected bootcamp/accelerator, or extensive mentorship program
• Successfully solve Callibrity’s CoderPad and Code Pairing Challenges
• Interpersonal skills: Influencer, professionalism and accountability, passion for learning, us not them mentality, shared responsibility
• Embodies Callibrity’s Core Values
• Must be authorized to work in the United States, without sponsorship

Benefits of Being a Callibrity Consultant:

• Health/Vision/Dental insurance effective first day of employment
• 401k match vested immediately
• Profit Sharing Bonus
• Annual professional development stipend ($1,500)
• Paid parental leave
• Paid bench time
• Paid flex time for training, blogging, speaking engagements, and thought leadership
• Paid time off (3–5 weeks depending on level) + 8 holidays
• Competitive industry salary
• Lunch and learns
• Team building events
• Small company feel with the stability of a proven business model
• High employee retention and satisfaction (One of Cincinnati’s Best Places to Work!)

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.

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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).

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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Sr. ML Data Engineer, Relevancy Sciences – Personalization & Loyalty Strategy (P508)

The Relevancy Sciences Team is responsible for creating relevant and personalized customer experiences for Kroger's E-commerce platform, which ranks among the top 10 ecommerce companies in the US. We generate trillions of recommendations at scale and deliver them to millions of Kroger customers daily. Our team maintains a comprehensive portfolio of machine learning solutions for search & product recommendations. We are seeking a talented and experienced Senior ML Data Engineer to join our data science team, with specialized expertise in building search and recommender systems.

Role Overview

You will architect, build, and operate the critical data infrastructure that powers our machine learning models, spanning from feature engineering to training data generation. This role serves as the bridge between ML requirements and production data systems, with ownership of feature stores, training/evaluation pipelines, and ML-specific data operations. You will enable data scientists to iterate rapidly while ensuring production-grade reliability and scalability.

What You'll Do

1. Feature Store Operations & Governance (40%)

• Own the feature request lifecycle from intake through deployment, driving reusability and maintaining a searchable feature catalog
• Design and build scalable feature pipelines that compute features from diverse sources (BigQuery, Azure Data Lake) and write to Feature Store infrastructure (Vertex AI Feature Store + BigQuery)
• Build streaming feature engineering pipelines using Apache Beam/Dataflow for real time feature computation and low-latency model serving with sub-second data freshness
• Ensure point-in-time correctness and online/offline feature consistency to prevent data leakage
• Implement drift detection, data quality monitoring, and alerting mechanisms
• Develop self-service tools and templates that enable teams to independently create features

1. Training & Evaluation Data Pipelines (30%)

• Build automated pipelines that generate ML-ready training datasets by combining features with labeled target variables
• Implement point-in-time correctness logic and sophisticated sampling strategies to ensure balanced, representative datasets
• Maintain comprehensive dataset versioning for full traceability across model versions
• Generate detailed evaluation reports with performance metrics segmented by business dimensions
• Support operations across both Azure and Vertex AI environments during platform migration

1. ML Data Operations & Reliability (20%)

• Serve as Tier 2/3 on-call responder for feature data quality incidents, diagnosing and resolving pipeline failures and performance issues
• Maintain comprehensive lineage tracking and metadata management for full data traceability
• Support regulatory compliance through proper data governance and documentation

1. Standards, Education & Collaboration (10%)

• Establish and enforce feature naming conventions, data quality thresholds, and point-in-time correctness patterns
• Conduct workshops on feature engineering best practices and provide expert guidance on feature design
• Partner with Data Scientists, ML Engineers, Data Engineering, and MLOps teams to optimize infrastructure and align with technical strategy

What We're Looking For

Required Qualifications:

• 3+ years of hands-on experience building and maintaining ML data pipelines in production environments with demonstrated expertise in scaling and reliability
• Expert-level SQL skills and advanced Python programming capabilities with experience in data processing frameworks and ML libraries
• Proven experience with cloud data platforms, with strong preference for GCP ecosystem including BigQuery, Dataflow, Vertex AI Feature Store, and associated ML services
• Deep understanding of end-to-end ML workflows including training data preparation, model evaluation methodologies, and serving infrastructure requirements
• Production operations mindset with experience in monitoring, alerting, on-call responsibilities, and meeting SLA commitments

Strongly Preferred Qualifications:

• Hands-on experience with Feature Store platforms such as Vertex AI Feature Store, Feast, Tecton, or similar enterprise solutions
• Deep knowledge of point-in-time correctness principles, temporal joins, and time-series data modeling best practices
• Multi-cloud experience with both Azure and GCP platforms, including data migration and hybrid cloud architectures
• Strong familiarity with core ML concepts including feature engineering, label creation, train/test/validation splits, and data leakage prevention
• Background spanning both analytics engineering and ML-specific data engineering with understanding of the unique requirements of each domain

Success Indicators:

• Improved Data Science Productivity:Data Scientists spend significantly less time on data preparation and infrastructure concerns, enabling more focus on model development and experimentation
• Increased Feature Reuse:Measurable increase in feature reuse across multiple models and teams, reducing redundant development effort and improving consistency
• Reliable Automation:Training and evaluation data generation processes operate reliably with minimal manual intervention and high uptime
• Efficient Incident Response:Data quality incidents are triaged quickly with clear escalation paths and rapid resolution times
• Accelerated ML Iteration:Overall ML model development and iteration velocity improves measurably across all teams using the platform

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