Senior Manager, AI Cloud Innovation Engineer

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

Estimated Travel: 0-10%

Direct Reports: None

The Global Equipment Platforms (GEP) team is seeking an exceptional and forward-thinking AI & Cloud Innovation Engineer to spearhead the exploration, prototyping, and strategic integration of cutting-edge Artificial Intelligence and advanced cloud capabilities for The Coca-Cola Company's global fleet of 17MM+ connected equipment. Reporting to the AI & Digital Innovation Lead within GEP Digital, Data, & AI, this individual contributor role is pivotal in transforming the insights generated by our Data Scientists into tangible, deployed capabilities that reduce operating expenses, increase revenue, and provide unprecedented real-time market understanding.

You will be responsible for researching, experimenting with, and building proofs-of-concept (POCs) utilizing nascent AI technologies (e.g., advanced Generative AI applications, cutting-edge computer vision techniques, responsible AI frameworks, sophisticated AI Agents) and novel cloud architectures (e.g., edge computing for IoT, serverless AI patterns, highly distributed compute models on Azure). This role demands deep technical expertise in both AI and cloud environments, coupled with a relentless curiosity and a proven ability to translate abstract ideas into tangible, demonstrable prototypes. Your work will directly inform the future roadmap of our Unified IoT solution, ensuring we can strategically leverage breakthroughs to further reduce TCO, increase transactions, and generate unparalleled real-time insights from our KO Operating System (KOS)-powered devices across a multi-tenant global landscape.

Key Responsibilities:

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

• Design, develop, and maintain robust, scalable MLOps pipelines for deploying, monitoring, retraining, and versioning machine learning models, AI Agents, and computer vision algorithms.

• 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.

• 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.

• Research, prototype, and evaluate cutting-edge AI technologies (e.g., Generative AI, Edge AI, Computer Vision on KOS) to identify new opportunities, working closely with the Lead AI Engineer to establish feasibility for scalable production deployment and with the Product Owner to assess potential for new product features.

• Collaborate with Data Scientists on novel algorithmic approaches, providing expertise on implementation challenges and opportunities for product ionization.

Performance Monitoring & Optimization (15%):

• Implement comprehensive monitoring, logging, and alerting for deployed AI models, tracking performance metrics (e.g., latency, throughput, error rates), model drift, and data quality issues in production.

• Proactively identify bottlenecks and optimize the performance and cost-efficiency of AI inference and retraining pipelines.

• Establish best practices for model retraining strategies, including trigger conditions, data versioning, and A/B testing in production.

• Ensure the AI solutions are designed to take advantage of future connected device technology breakthroughs (e.g., LoRaWAN) and hardware advancements.

Key Deliverables:

• Robust, scalable, and highly available MLOps pipelines for the GEP AI ecosystem.

• Production-ready deployed AI/ML models (e.g., predictive maintenance, computer vision, AI Agents) delivering measurable business value.

• Optimized AI inference services with clear APIs for application integration.

• Comprehensive monitoring and alerting frameworks for deployed AI solutions.

• Documented AI engineering best practices, architecture patterns, and deployment guides.

• Successful enablement and adoption of AI-powered features by internal and external stakeholders.

Decision Rights:

• Technical design and implementation details for MLOps pipelines and AI model serving infrastructure.

• Selection of specific AI engineering tools and libraries (within approved Azure ecosystem guidelines).

• Optimization strategies for AI model performance, cost, and reliability in production.

Required Experience & Qualifications:

• Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related quantitative field. Master's or Ph.D. preferred.

• 7+ years of hands-on experience in AI/ML engineering, MLOps, or productionizing machine learning models in cloud environments.

• Expert-level proficiency in designing, building, and operating production-grade AI/ML pipelines on Microsoft Azure (e.g., Azure Machine Learning, Azure Kubernetes Service, Azure Functions, Azure Databricks).

• Strong software engineering background with extensive experience in Python, including developing robust, production-quality code and APIs.

• Proficiency with containerization technologies (Docker) and orchestration platforms (Kubernetes).

• Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and deploying models trained with these frameworks.

• Solid understanding of cloud infrastructure concepts, networking, and security best practices relevant to AI deployments.

• Experience with Git and CI/CD tools (e.g., Azure DevOps, GitHub Actions).

• Familiarity with IoT, telemetry data, and embedded systems (exposure to KOS or similar OS is a plus).

• Proven ability to work independently and drive technical projects from conception to production.

Competencies:

• Engineering Excellence: Possesses deep software engineering principles and applies them rigorously to build robust, scalable, and maintainable AI solutions.

• AI Vision & Execution: Translates strategic AI concepts into practical, deployable systems, bridging research with real-world application.

• Problem Solver & Innovator: Tackles complex technical challenges in AI deployment, consistently seeking and implementing innovative solutions.

• Collaborative Integrator: Works effectively across diverse technical teams (Data Science, Data Engineering, IT) and with business stakeholders to ensure seamless AI integration.

• Results-Driven & Accountable: Focuses on delivering tangible business value through deployed AI, taking ownership of the end-to-end operational success of solutions.

• Continuous Learner: Stays abreast of the rapidly evolving AI landscape and proactively adopts new technologies and best practices.

Success is measured by:

• Productionized AI Solutions: Number and diversity of AI/ML models, AI Agents, or computer vision solutions successfully deployed and operating in production.

• Operational Performance: Uptime, latency, and throughput of deployed AI services; adherence to defined SLAs.

• Cost Efficiency: Optimization of compute and storage costs for AI workloads in production.

• Business Impact: Measurable contribution to TCO reduction, revenue uplift, and enhancement of real-time market insights via AI-powered features.

• Deployment Velocity: Reduction in time from model readiness to production deployment.

• Model Reliability: Reduction in production incidents related to AI model serving and performance.

What We Can Do for You

• Iconic & Innovative Brands: Our portfolio represents over 250 products with some of the most popular brands in the world, including Coca-Cola, Simply, Fairlife & Topo Chico.

• Expansive & Diverse Customers: We work with a diversified group of customers which range from retail & grocery outlets, theme parks, movie theatres, restaurants, and many more each day. 

Skills

Containerization Software; Microsoft Azure Databricks; Microsoft Azure Functions; Machine Learning Operations; AI Programming; Artificial Intelligence (AI); Azure Kubernetes Service (AKS); Git; Docker (Software); Microsoft Azure Machine Learning; GitHub; Microsoft Azure DevOps; Machine Learning