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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI DevOps Engineer (Global) - **Company:** MGT Impact Solutions, LLC - **Location:** United States (Remote available) - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, Cloud Computing, Cloud Engineering, Continuous Integration, DevOps, Github, Monitoring of Systems, Python (Programming Language), Machine Learning, Ansible, Prometheus, Software Deployment, AI Infrastructure, Data Logging, Google Cloud, Cloud Platform System, Delivery Pipeline, Large Language Models, Grafana, Software Troubleshooting, Reliability of Systems, Containerization, AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Deployment Automation, Machine Learning Operations, Terraform, Data Pipelines, Docker - **Published:** July 18, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=8eac67db313991b1 ## About the Role * Hands-on experience deploying and managing machine learning models in production environments. * Strong knowledge of containerization technologies and orchestration platforms. * Experience building and maintaining CI/CD pipelines. * Hands-on experience with Infrastructure-as-Code tools and cloud-native environments. * Familiarity with monitoring, logging, and observability solutions. * Strong understanding of security best practices for cloud and AI infrastructure. * Excellent written and verbal English communication skills. * Ability to work independently in a fully remote, U.S.-aligned environment. * Strong troubleshooting, problem-solving, and cross-functional collaboration skills., * Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field preferred, or equivalent professional experience. * Three (3) or more years of experience in DevOps, MLOps, platform engineering, or related infrastructure roles. * Experience working with government, education, or other regulated public sector organizations preferred. * Familiarity with compliance frameworks such as FedRAMP, NIST, or similar regulatory standards preferred. * Experience supporting LLM deployment pipelines, generative AI infrastructure, or AI platforms preferred. * Experience with MLOps frameworks and model lifecycle management preferred. * Cloud certifications, including AWS, Azure, or GCP, are a plus. * Experience working in consulting or client-facing technical environments preferred., * DevOps and MLOps * Cloud Infrastructure Management * Kubernetes and Container Orchestration * CI/CD Pipeline Development * Infrastructure as Code * AI and Machine Learning Deployment * Monitoring and Observability * Security and Compliance * Problem Solving and Troubleshooting * Communication and Cross-Functional Collaboration * Ownership and Execution ## Description The AI DevOps Engineer will join MGT's AI Operating Group, a team focused on building and deploying AI-powered solutions for state and local government, education, and other public sector organizations. This role bridges the gap between AI development and production infrastructure, ensuring that machine learning models, AI applications, and data pipelines are deployed securely, reliably, and at scale. The ideal candidate combines strong DevOps expertise with experience supporting AI and machine learning workloads in cloud-native environments. In this role, you will: * Build, maintain, and optimize CI/CD pipelines for AI and machine learning deployments. * Deploy and manage containerized AI workloads using Docker and Kubernetes. * Monitor production environments, model performance, infrastructure health, and system reliability. * Collaborate with AI engineers, data scientists, and solution architects to streamline deployment processes. * Implement Infrastructure-as-Code practices to improve scalability, consistency, and reproducibility. * Manage cloud infrastructure and platform services across AWS, Azure, and GCP environments. * Enforce security, compliance, and access control standards for AI systems. * Troubleshoot infrastructure and deployment issues while supporting incident response efforts. * Create and maintain operational documentation, deployment procedures, and technical runbooks. * Improve observability, monitoring, logging, and alerting frameworks for AI platforms., * Python * Docker * Kubernetes * Terraform * GitHub Actions * AWS / Azure / GCP * MLflow * Apache Airflow * Prometheus * Grafana * Ansible SUCCESS MILESTONES: 30 Days * Gain familiarity with MGT's AI platforms, cloud environments, and deployment standards. * Understand active client engagements and operational workflows. * Contribute to infrastructure improvements and deployment processes. 90 Days * Independently manage AI deployment pipelines and production infrastructure. * Improve automation, monitoring, and deployment reliability across projects. * Support successful releases of AI and machine learning solutions for client engagements. 6 Months * Lead infrastructure initiatives for complex AI deployments. * Establish DevOps and MLOps best practices across the organization. * Drive improvements in scalability, security, observability, and operational excellence. * Serve as a technical resource for AI platform architecture and deployment strategy. ## Related Videos - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [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) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [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) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [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) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud)