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AI DevOps Engineer – Global
MGTAI DevOps Engineer deploying AI-powered solutions for state and local government and education sectors. Ensuring secure and scalable deployment of machine learning models and AI applications across cloud environments.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and optimizing CI/CD pipelines for AI and machine learning deployments, along with strong capabilities in managing cloud infrastructure across AWS, Azure, and GCP. Proficient in containerization technologies, Infrastructure-as-Code practices, and ensuring security compliance for AI systems.
Highest-signal resume keywords
CI/CD Pipeline DevelopmentContainerization TechnologiesInfrastructure-as-CodeCloud Infrastructure ManagementSecurity Best Practices
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
Machine Learning Model DeploymentMonitoring and Observability SolutionsTroubleshooting and Problem-SolvingMLOps FrameworksModel Lifecycle Management
Soft Skills
Cross-Functional CollaborationExcellent Communication Skills
Tools & Technologies
DockerKubernetesAWSAzureGCP
Certifications & Qualifications
AWS CertificationAzure CertificationGCP Certification
Industry Keywords
FedRAMPNISTRegulated Public SectorAI PlatformsGenerative AI Infrastructure
Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformKubernetes
About the role
Key responsibilities & impact- 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.
Requirements
What you’ll need- 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.
Benefits
Comp & perks- Flexible paid time off
- 5% 401K matching program
- Equity opportunities
- Incentive and bonus programs
- Up to 16 weeks of paid parental leave
- Flexible spending accounts
- Full-health benefits with base employee coverage fully funded, comprising:
- Medical, dental, and vision coverage
- Life insurance
- Short and long-term disability coverage
- Income protection benefits