Design, implement, and manage CI/CD pipelines using tools such as Jenkins, GitHub Actions, or Azure DevOps
Develop and maintain Infrastructure-as-Code using Terraform
Manage and scale container orchestration environments using Kubernetes, including experience with larger production-grade clusters
Ensure cloud infrastructure is optimized, secure, and monitored effectively
Collaborate with data science teams to support ML model deployment and operationalization
Implement MLOps best practices, including model versioning, deployment strategies (e.g., blue-green), monitoring (data drift, concept drift), and experiment tracking (e.g., MLflow)
Build and maintain automated ML pipelines to streamline model lifecycle management

Requirements

8 to 12 years of experience in DevOps and/or MLOps roles
Proficient in CI/CD tools: Jenkins, GitHub Actions, Azure DevOps
Strong expertise in Terraform, including managing and scaling infrastructure across large environments
Hands-on experience with Kubernetes in larger clusters , including workload distribution, autoscaling, and cluster monitoring
Strong understanding of containerization technologies (Docker) and microservices architecture
Solid grasp of cloud networking, security best practices, and observability
Scripting proficiency in Bash and Python
Experience with MLflow, TFX, Kubeflow, or SageMaker Pipelines (preferred)
Knowledge of model performance monitoring and ML system reliability (preferred)
Familiarity with AWS MLOps stack or equivalent tools on Azure/GCP (preferred)

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

CI/CDTerraformKubernetesDockerBashPythonMLflowTFXKubeflowSageMaker Pipelines