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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Architect - Not an Active Opening, Building Talent Pipeline in , United States - **Company:** Energy Jobline - **Location:** United States (Remote available) - **Salary:** $140,000.0 - $157,500.0 - **Contract:** Permanent contract - **Skills:** Mxnet, Testing (Software), Amazon Web Services, Build Automation, Cloud Computing, Continuous Integration, Machine Learning, Tensorflow, Subsystems, Data Logging, Comet Programming, Feature Engineering, Pytorch, Delivery Pipeline, Cloudformation, Git Flow, Scikit Learn, Integration Tests, Infrastructure Automation Frameworks, Machine Learning Operations, Terraform - **Published:** July 30, 2026 - **Apply:** https://www.energyjobline.com/job/machine-learning-architect-not-active-opening-building-talent-pipeline-united-states-31284044 ## About the Role Confidently addresses client questions and identifies gaps in requirements with proposed next steps. Focus on delivering clear, actionable solutions that meet client goals Collaboration Facilitates alignment across stakeholders or technical priorities and deliverables. Navigates differing perspectives with professionalism to ensure shared understanding and progress. Communication Breaks down complex technical concepts for non-technical audiences in a clear, concise manner. Adapts communication style based on audience and context to improve clarity and decision-making. Technical Qualifications Experience with IaC tools (CloudFormation, CDK, Terraform). Expert level experience in Amazon SageMaker and ML libraries (TensorFlow, MXNet, PyTorch, Scikit-learn). Strong understanding of ML concepts: feature engineering, hyperparameter tuning, optimization strategies. Familiarity with MLOps tools (MLflow, Neptune, Comet). ## Description We are seeking an exceptional Machine Learning Architect to join our growing Cloud Applications team. The right candidate is someone who has deep expertise in ML system design and is passionate about working with our customers, partners, and colleagues to drive innovation forward. Your mission will be to work alongside Caylent's Engineers, Engineering Managers, and Project Managers to deliver AWS solutions across our diverse and forward-thinking customer base. You'll work with the latest technologies and support customers looking to bring cutting-edge ideas to market. Your Assignment You will be a mission control specialist, guiding Cayliens and Customers alike through Agile ceremonies like stand-ups, retrospectives, and more. You will translate customer requirements and into a workable backlog of tickets for engineers. Delegate tickets to a team of engineers in order to complete customer projects. Lead requirements gathering, backlog grooming, and architecture discussions. Apply your understanding of DevOps pipelines, including build automation, branching strategies, CI/CD, Infrastructure as Code, security, monitoring, logging, and alerting. Troubleshoot and resolve issues in customer dev, test, and production environments. Automate software testing at multiple levels (component, configuration item, subsystem, system) and monitor results. Write production quality code, including unit and integration tests. Work with a team to deliver top-quality cloud applications on AWS for customers. Clearly communicate and document your designs, processes, and procedures. You will demonstrate a passion to Stay Curious as you mentor peers, tackle new technologies, and learn from our word-class team of engineers. Coach and mentor less experienced teammates, coach and be mentored by world class engineers. ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Effective Machine Learning - Managing Complexity with MLOps](https://www.wearedevelopers.com/videos/185-effective-machine-learning-managing-complexity-with-mlops) - [Why Git Still Matters](https://www.wearedevelopers.com/videos/100288-why-git-still-matters) - [Serverless deployment of (large) NLP models ](https://www.wearedevelopers.com/videos/158-serverless-deployment-of-large-nlp-models) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [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) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)