ML Ops Technical Specialist - DevOps, Python

Role overview

Responsibilities

• Automate model training, deployment, and monitoring using ML Ops frameworks.
• Design and manage machine learning CI/CD pipelines.
• Create infrastructure-as-code templates for scalable cloud ML environments.
• Integrate monitoring and logging for performance tracking and issue resolution.
• Configure version control for collaborative and reproducible development.
• Advise on ML Ops practices, tools, and workflow optimization; mentor and train colleagues.
• Review deliverables against client specifications and quality standards.
• Recommend and implement client-focused improvements using ML Ops frameworks and industry practices.

Skills and experience

• Expert proficiency in MLflow, Kubeflow Pipelines, TFX, and Metaflow.
• Strong Python skills for ML pipeline development and automation.
• Expert knowledge of Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions.
• Advanced Terraform, AWS CloudFormation, and Ansible proficiency.
• Strong understanding of AWS, Azure, and GCP for scalable ML deployments.
• Expertise with Prometheus, Grafana, ELK Stack, and Fluentd; strong command of Git, GitHub, GitLab, and Bitbucket.
• Strong Bash and PowerShell scripting abilities and solid end-to-end ML pipeline design and optimization experience.

Optional credentials

• AWS Certified Machine Learning Specialty, Google Professional Machine Learning Engineer, or HashiCorp Certified: Terraform Associate are described as optional but valuable.