MLOps / DevOps Engineer

Description Hybrid 3 on in Charlotte, NC Our client seeks a senior MLOps / DevOps Engineer focused on platform and infrastructure. The role emphasizes building and operating reliable, scalable, and automated cloud environments that enable machine learning development and deployment. You will collaborate with data scientists, platform teams, and product owners to provision infrastructure as code, implement CI/CD automation, and improve developer experience through tooling and documentation. Due to client requirements, applicants must be willing and able to work on a w2 basis. For our w2 consultants, we offer a great benefits package that includes Medical, Dental, and Vision benefits, 401k with company matching, and life insurance. Rate: $70.00 to $81.00/hr. w2 JN -062026-107530 Responsibilities Design, build, and maintain cloud infrastructure using Terraform. Develop and manage CI/CD pipelines with GitHub Actions for build, test, and deployment automation. Support and improve MLOps platforms with a focus on reliability, scalability, and automation. Debug and resolve issues across Terraform deployments, pipelines, and cloud services. Automate environment provisioning, configuration, and lifecycle management. Implement monitoring, logging, and alerting to ensure platform health and performance. Collaborate with data scientists and engineers to streamline model deployment and promotion to production. Contribute to repository management, version control practices, and code quality standards. Drive developer experience improvements through tooling, documentation, and self-service capabilities. Experience Requirements Strong experience with Terraform for infrastructure provisioning and automation. Proficiency in Python for automation, tooling, and integrations. Hands-on experience with GitHub Actions or similar CI/CD systems, including pipeline design and migration. Strong understanding of Git operations, CI/CD principles, and DevOps practices. Experience with AWS. Experience with containerization and orchestration. Familiarity with the machine learning lifecycle and model deployment workflows. Experience supporting ML platforms, data pipelines, or model infrastructure preferred. Strong communication skills and ability to work cross-functionally. Problem-solving mindset with attention to detail and shared ownership. Education Requirements Bachelor's degree in technical field or commensurate industry experience.