Lead Cloud Engineer (P990)

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.

Join us at 84.51°!

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Lead Cloud Engineer P990

OVERVIEW:
This role will operate within our technology organization, working across multiple product areas and business domains. The Senior Engineer will play a critical role in shaping technical architecture, driving modernization efforts, and enabling cross-functional collaboration across teams. With a focus on cloud engineering (Azure), Databricks, API development, Snowflake, and artificial intelligence/machine learning (AI/ML), this role will help set the technical direction for the organization while providing hands-on implementation and mentorship.

The ideal candidate is both a strategic thinker and a doer, capable of diving deep into legacy systems, modernizing them, and driving adoption of new technologies and practices across the organization. Additionally, this role will focus on scaling AI/ML capabilities across the enterprise, developing proof of concepts (POCs), and enabling teams to leverage machine learning for impactful use cases.

This role will require working across the stack — from understanding legacy components to building entirely new solutions with organization-wide impact. A key focus will be enabling migrations to the cloud, scaling AI/ML models, and supporting “day 2” operations with a customer-enablement mindset. The goal is to empower teams to solve their challenges using common tooling and shared solutions, while reducing duplication of effort and driving enterprise-wide efficiency.

RESPONSIBILITIES:

• Technical Leadership:
• Design, develop, test, implement, and support scalable technical architectures across cloud, data, AI/ML, and API domains.
• Discover common underlying patterns and provide technical guidance and documentation for best practices (e.g., CI/CD, monitoring, logging, API development, data pipelines, AI/ML pipelines).
• Act as a thought leader and subject matter expert across multiple teams and domains, ensuring alignment with organizational goals.
• Cloud, Data, and AI Modernization:
• Lead efforts to modernize legacy systems and shape the organization's transition to cloud-native solutions, particularly on Azure.
• Build and maintain integrations with Snowflake, Databricks, and other data platforms, enabling seamless data sharing and insights.
• Develop and deploy scalable APIs and microservices that support cross-functional business needs while ensuring maintainability.
• Design and implement scalable AI/ML pipelines and workflows to support enterprise-wide AI initiatives.
• AI/ML Enablement & POCs:
• Partner with data science teams and business stakeholders to identify AI/ML use cases and develop proof of concepts (POCs) to validate their feasibility and impact.
• Scale successful AI/ML POCs into production-grade solutions, ensuring reliability, performance, and maintainability.
• Collaborate with teams to implement MLOps best practices for model deployment, monitoring, and governance.
• Identify opportunities to integrate AI/ML into existing systems and workflows to drive automation and insights.
• Mentorship & Enablement:
• Mentor team members and cross-train engineers across the organization to elevate technical skills and adoption of modern practices.
• Conduct internal training sessions and showcase new technologies, including AI/ML advancements, to drive adoption and understanding.
• Proof of Concept & Implementation:
• Work with teams to design and execute proof of concepts for new technologies and tools, with the goal of scaling successful solutions across the organization.
• Evaluate and implement automation tools (e.g., Terraform, Helm) and cloud-native services to improve efficiency and reliability.
• Incident Management & Continuous Improvement:
• Troubleshoot large-scale incidents alongside other technology teams, driving resolution and identifying opportunities for prevention.
• Participate in post-incident retrospectives and implement improvements to reduce future risks.
• Standardization & Tooling Optimization:
• Drive adoption of common tooling and reduce duplication of effort across teams by standardizing processes and technologies.
• Incorporate feedback from users and stakeholders to continuously improve platform services, AI/ML pipelines, and shared solutions.

QUALIFICATIONS, SKILLS, AND EXPERIENCE:

• Education & Work Experience:
• Bachelor’s degree in IT, Computer Science, or related field
• Proven track record of working across cloud engineering, data platforms, AI/ML, and API development in a senior technical capacity.
• Technical Expertise:
• Deep understanding of modern software deployment and architecture patterns, including containerization, CI/CD pipelines, and monitoring/logging.
• Strong experience with Azure cloud services, including resource provisioning, networking, and cost optimization.
• Hands-on experience with Databricks, Snowflake, and building scalable data pipelines and integrations.
• Expertise in API design and development, including RESTful and event-driven architectures.
• Experience in designing, deploying, and scaling AI/ML models in production environments.
• Familiarity with MLOps practices, tools, and frameworks for managing the machine learning lifecycle.
• Hands-on experience with automation and IaC tools like Terraform and Helm, as well as orchestration platforms.
• AI/ML Experience:
• Strong understanding of AI/ML concepts, frameworks, and tools (e.g., TensorFlow, PyTorch, MLflow, Azure Machine Learning).
• Experience in building and deploying AI/ML models at scale, including data preprocessing, training, and model monitoring.
• Ability to work with data scientists and translate business problems into AI/ML solutions.
• Cross-Functional Collaboration:
• Ability to work across teams and domains, serving as a bridge between engineering, data, AI, and business teams.
• Strong interpersonal and communication skills to mentor, document, and present technical solutions effectively.
• Problem Solving & Innovation:
• Ability to dive deeply into complex systems, identify opportunities for improvement, and drive modernization efforts.
• Passion for staying up-to-date on technology trends, including AI/ML advancements, and applying them thoughtfully to solve organizational challenges.
• Certifications & Tools:
• Certifications in relevant technologies (e.g., Azure Architect, Databricks Certified Developer, Kubernetes Administrator, Azure AI Engineer) are a plus.
• Familiarity with tools such as GitHub Actions, Datadog, Dynatrace, Grafana, Prometheus, and ServiceNow preferred.

TECHNOLOGIES IN USE (FOR REFERENCE):

• Cloud: Azure (preferred), AWS, GCP
• Data: Snowflake, Databricks, Azure Data Factory
• AI/ML: Azure Machine Learning, MLflow, TensorFlow, PyTorch
• Automation: Terraform, Helm
• Monitoring & Logging: Datadog, Grafana, Prometheus, Dynatrace
• CI/CD & DevOps: GitHub Actions, Harness CD, Artifactory

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