> Markdown version of [/jobs/ext/2653256-machine-learning-architect-ii](https://www.wearedevelopers.com/jobs/ext/2653256-machine-learning-architect-ii). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Architect II - **Company:** The Coca-Cola Company - **Location:** Atlanta, GA, United States - **Experience:** Experienced - **Salary:** $143,400.0 - $169,300.0 - **Contract:** Internship / Graduate position - **Skills:** Artificial Intelligence, Microsoft Azure, Cloud Computing, Computer Programming, Information Engineering, Data Integration, DevOps, Github, Python (Programming Language), Machine Learning, Azure Machine Learning, Software Engineering, Data Logging, Scripting, Delivery Pipeline, Git, Microsoft Fabric, Containerization, Kubernetes, Azure AKS, Machine Learning Operations, Software Version Control, Data Pipelines, Docker - **Published:** August 6, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=a3baaefe539df3d2 ## About the Role * 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 Practical experience with as many of the following as possible: * Handles multiple competing priorities in a fast-paced, deadline-driven environment * Strong attention to details and excellent problem-solving skills * Ability to work in a collaborative team environment * Highly innovative, adaptable, and self-directed * Results-oriented with a delivery focus * Presentation skills: Ability to communicate technical topics to business audience. * Be able to collaborate across other levels of the organization * Team player who can lead a discussion to defined outcomes * Effective Communication * Pursuing Innovation What We Can Do for You: * Innovation & Technology: The ability to work with an award-winning team that is on the cutting edge of innovation. ## Description 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. ## Related Videos - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) ## Related Articles - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts)