Senior Manager, AI Engineering & Transformation

Location(s):

United States of America

City/Cities:

Atlanta

Travel Required:

00% - 25%

Relocation Provided:

No

Job Posting End Date:

September 12, 2025

Shift:

Job Description Summary:

Location: Atlanta, GA (Global HQ) - hybrid, onsite 3 days/week

Estimated Travel: 0-10%

Direct Reports: None

The AI Engineer & Transformation for Global Equipment Platforms (GEP) will be critical in operationalizing and scaling AI capabilities across Coca-Cola's 17MM+ connected equipment fleet. This role is responsible for designing, building, and maintaining robust MLOps pipelines and production infrastructure for machine learning models, AI Agents, and computer vision solutions, bridging the gap between data science research and reliable, large-scale deployment to drive significant business impact.

You will play a hands-on role in deploying AI across cloud and edge environments (including KOS-enabled devices), ensuring high performance, reliability, and cost-effectiveness. Your work will directly contribute to reducing equipment Total Cost of Ownership (TCO), increasing transactions, and providing unprecedented real-time market insights that eliminate the "fog of war" for Coca-Cola's global operations, bottlers, and OEM partners. This role demands a strong engineering mindset, deep expertise in cloud-native AI services (Azure preferred), and a passion for turning cutting-edge AI research into tangible business value.

Key Responsibilities:

AI/ML Model Implementation & MLOps (40%):

• Design, develop, and maintain robust, scalable MLOps pipelines for the entire ML lifecycle, including data versioning, model training, model versioning, testing, deployment, and monitoring, ensuring reproducibility and reliability.

• Design, build, and maintain robust MLOps pipelines and scalable AI infrastructure on Azure, operationalizing models developed by Data Scientists and integrating successful innovations from the AI & Cloud Innovation Engineer into the Unified IoT Ecosystem and KOS, ensuring high performance, reliability, and multi-tenant capabilities.

• Implement automated CI/CD processes for AI artifacts, ensuring rapid and reliable deployment of models into production environments (e.g., Azure ML, Azure Kubernetes Service).

• Work hands-on to containerize (e.g., Docker) and orchestrate (e.g., Kubernetes) AI services for efficient resource utilization and high availability across the global equipment fleet.

• Develop and manage API endpoints for AI models, ensuring secure, low-latency, and high-throughput inference services for consumption by applications and other systems.

AI Infrastructure & Ecosystem Integration (25%):

• Collaborate with Lead Data Engineers and Digital Technology Solutions (IT) to provision, configure, and optimize cloud-based AI infrastructure (e.g., GPU clusters, specialized compute instances) on Azure.

• Integrate AI capabilities seamlessly into existing GEP applications and platforms, including remote equipment management tools, content management systems, marketing solutions, and analytics dashboards.

• Design and implement data contracts and integration patterns between AI services and the core IoT platform, ensuring efficient data flow for real-time inference and model updates.

• Ensure the AI solutions are generic enough to support varied global market needs and can operate across different equipment types (dispense, vend, cooler, racks).

Advanced AI Exploration & Transformation (20%):

• Research, prototype, and engineer solutions for emerging AI technologies, including the operationalization of AI Agents for autonomous decision-making and advanced computer vision algorithms for real-time insights from equipment.

• Drive the "transformation" aspect by actively enabling internal teams, bottlers, and OEMs to adopt and leverage AI-powered features, demonstrating their value and providing technical enablement for both internal and external use cases. 

• Work closely with Data Scientists to transition experimental models into production-grade solutions, ensuring scalability, reliability, and maintainability.

• Identify opportunities to apply AI to reduce equipment TCO, increase transactions, and provide deeper market insights, turning the 17MM+ pieces of equipment into intelligence assets.

• Work in tandem with the AI & Cloud Innovation Engineer to productionize novel AI solutions and ensure continuous knowledge transfer for emerging technologies and best practices.

Performance Monitoring &