Machine Learning Architect II

Summary: The Coca-Cola Company’s Technology organization is   in the midst of   a digital transformation that allows our employees to use world class technology to connect our products to our customers all over the world. This journey is a very exciting time for   Coca-Cola   and our employees are big contributors to our Success and Growth. Our large scale and complex environment   offers   an incredible opportunity to address challenges, enable innovative solutions to make a difference for our customers.     In this position, you will embark on a journey of leveraging vast amounts of data to transform it into actionable insights. You will aid in the development of analytics models and work under the guidance of seasoned data science professionals to drive decision-making and strategy   across the organization. This is an exciting opportunity to   grow in your career in data science and analytics within a supportive and innovative environment.     What You’ll Do for Us:   Model Deployment & Operationalization: Partner with data science teams to transition machine learning models from experimentation to production environments, packaging models into robust Docker containers for scalable and reproducible deployments.   Pipeline Automation: Build and maintain automated CI/CD pipelines for machine learning workflows (e.g., model training, evaluation, and deployment) utilizing tools like GitHub Actions. Leverage Azure Container Registry to securely manage container images and deploy scalable workloads to Azure Kubernetes Service (AKS) or Azure Container Instances (ACS).   Utilize Azure Machine Learning and Microsoft Fabric Data Science to manage the ML lifecycle. Adapt prior experience from other cloud platforms to effectively navigate and optimize our current stack.   Monitoring & Maintenance: Implement monitoring solutions to track model performance, data drift, and system health in production. Ensure comprehensive logging and observability for containerized model endpoints running on Kubernetes clusters. Troubleshoot and resolve operational issues as they arise.   Data Integration: Collaborate with data engineering teams to ensure clean, reliable data pipelines (such as Medallion architectures) seamlessly feed into machine learning models.   Engineering Best Practices: Write clean, modular, and testable code (primarily in Python) while adhering to version control best practices using Git.   Mentor, guide, and develop junior/aspiring   MLOps   Engineer   across the organization.    Lead continuous career development and drive engineering excellence through performance reviews.     Qualifications & Requirements:   6 +   years of professional experience (or equivalent strong academic/internship experience) in   MLOps , Data Engineering, Software Engineering, or a related field.   3+ years of experience managing and scaling high-performing   MLOps   or data platform teams, with a focus on career development, performance management, and technical mentorship.   Cloud ML Platforms: Hands-on experience with at least one major cloud ML platform. While Azure ML and Microsoft Fabric are preferred, experience with AWS SageMaker, GCP Vertex AI, or similar platforms is highly acceptable.   Programming: Strong proficiency in Python for scripting, automation, and model deployment.   DevOps & Containerization: Familiarity with version control (Git), building CI/CD pipelines (e.g., GitHub Actions, Azure DevOps), and containerization ecosystems (Docker, Azure Container Registry, Kubernetes/AKS/ACS).   Foundational Knowledge: A solid understanding of the machine learning lifecycle, containerized microservices architectures, and fundamental software engineering principles.     Functional Skills:      Practical experience with as many of the following as possible:   Handles multiple competing priorities in a fast-paced, deadline-driven environment    Strong attention to