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Machine Learning Operations Engineer II - CliftonLarsonAllen
ML Engineer
Experienced ML Operations Engineer to design, deploy, and maintain scalable machine‑learning pipelines on cloud infrastructure, leveraging Python, Docker, Kubernetes, and CI/CD practices.
About the role
Key Responsibilities
- Design, build, and maintain end‑to‑end MLOps pipelines for model training, validation, and serving.
- Containerize machine‑learning workloads using Docker and orchestrate them with Kubernetes on AWS.
- Implement CI/CD workflows to automate model versioning, testing, and deployment.
- Monitor production models for performance drift, latency, and resource utilization, and trigger automated remediation.
- Collaborate with data scientists and software engineers to translate research prototypes into production‑ready services.
Requirements
- 3+ years of hands‑on experience in MLOps or DevOps roles, with strong Python programming skills.
- Proficiency in container technologies (Docker) and orchestration platforms (Kubernetes).
- Experience managing cloud resources on AWS, including EC2, S3, ECR, and SageMaker.
- Solid understanding of CI/CD tools such as Jenkins, GitLab CI, or GitHub Actions.
- Familiarity with machine‑learning frameworks (TensorFlow, PyTorch, Scikit‑learn) and model lifecycle management.
Skills
pythondockerkubernetesawscicdmachine learning