MLOps Engineer / DevOps Engineer

*Candidates must be legally authorized to work in the United States on a permanent and ongoing basis without the need for current or future employer-sponsored visa support, including H-1B, OPT, STEM OPT, or any other work authorization requiring sponsorship. Applications from candidates requiring sponsorship now or in the future will not be considered.

• Design, implement, and maintain scalable AWS cloud infrastructure supporting software, AI, and machine learning applications.
• Create and manage MLOps infrastructure for model training, deployment, monitoring, versioning, and lifecycle management.
• Partner closely with the Machine Learning Engineer to establish the tools, workflows, and infrastructure required for successful AI development and deployment.
• Support Generative AI initiatives by building infrastructure and deployment frameworks for applications utilizing AWS Bedrock, foundation models, LLMs, and related AI services.
• Build and manage Infrastructure as Code (IaC) using Terraform to ensure repeatable, secure, and scalable environments.
• Implement monitoring, logging, observability, and alerting systems across software, infrastructure, and machine learning platforms.
• Continuously identify opportunities to improve engineering processes, reduce manual effort, increase automation, and improve system reliability.
• Develop and maintain CI/CD pipelines that enable rapid, reliable software and machine learning deployments.
• Optimize cloud environments for scalability, performance, availability, and cost efficiency.
• Support security, compliance, backup, disaster recovery, and operational best practices across all environments.
• Troubleshoot infrastructure, deployment, and application issues across development, testing, and production environments.
• Document infrastructure architecture, deployment processes, operational procedures, and engineering standards.
• Contribute to establishing best practices for DevOps, MLOps, cloud architecture, and AI operations.

Please Note: Chefman is unable to provide visa sponsorship for this position. Candidates must be legally authorized to work in the United States on a permanent and ongoing basis without the need for current or future employer-sponsored visa support, including H-1B, OPT, STEM OPT, or any other work authorization requiring sponsorship. Applications from candidates requiring sponsorship now or in the future will not be considered.

• 5+ years of experience in MLOps, DevOps, Site Reliability Engineering (SRE), Platform Engineering, or related software engineering roles.
• Strong hands-on experience with AWS services and cloud-native architecture.
• Experience building and supporting AI and machine learning platforms in AWS environments.
• Experience supporting AI, machine learning, and Generative AI applications in production environments.
• Experience working with AWS Bedrock, Generative AI services, foundation models, LLM-powered applications, or related AI infrastructure.
• Strong experience implementing Infrastructure as Code using Terraform.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Strong experience building and maintaining CI/CD pipelines and deployment automation.
• Experience supporting machine learning workflows, model deployment, monitoring, and MLOps platforms.
• Strong programming and scripting skills in Python, Bash, or similar languages.
• Experience with monitoring, logging, observability, and operational tooling.
• Strong troubleshooting, systems-thinking, and problem-solving abilities.
• Excellent communication and cross-functional collaboration skills.
• Proven track record of improving engineering processes, increasing operational efficiency, and scaling software platforms.
• Highly organized and detail-oriented with a passion for automation and continuous improvement.

• Experience with vector databases, retrieval-augmented generation (RAG), model serving, and AI infrastructure.
• Experience supporting connected devices, IoT platforms, embedded systems, or consumer technology products.
• Domain expertise in machine learning infrastructure, AI platforms, consumer applications, connected products, or similar technology environments.
• Experience working in fast-paced startup or high-growth product organizations.