**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 & 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. + Ensure the ability to provide Over-The-Air (OTA) updates of AI models, firmware, and content to the global equipment fleet via the Unified IoT Ecosystem, collaborating with the Product Owner for seamless deployment. **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:** Microsoft Azure Databricks; PyTorch; Microsoft Azure Functions; Machine Learning Operations; Docker (Software); Azure Kubernetes Service (AKS); GitHub; Python (Programming Language); Containerization Software; Tensorflow; Artificial Intelligence (AI); AI Programming; Git; Microsoft Azure Machine Learning; Microsoft Azure Internet of Things (IOT); IoT Applications All persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form (Form I-9) upon hire. Pay Range:$131,000 - $153,000 Base pay offered may vary depending on geography, job-related knowledge, skills, and experience. A full range of medical, financial, and/or other benefits, dependent on the position, is offered. Annual Incentive Reference Value Percentage:15 Annual Incentive reference value is a market-based competitive value for your role. It falls in the middle of the range for your role, indicating performance at target. We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class.