DevOps and MLOps Engineers streamline development and deployment pipelines to deliver software and machine learning solutions faster, safer, and more reliably. They ensure infrastructure is scalable, automated, and built for performance for both traditional applications and AI-driven systems. They combine DevOps best practices (CI/CD pipelines, containerization, monitoring, cloud infrastructure management) with MLOps specialized workflows (model training, versioning, deployment, monitoring in production). Their engineers are skilled in tools like Docker, Kubernetes, Jenkins, GitHub Actions, Terraform, and Ansible, and work across cloud platforms like AWS, Azure, and Google Cloud. For MLOps, they integrate technologies like MLflow, Kubeflow, DVC, and SageMaker for seamless ML model lifecycle management.