ML Ops Technical Specialist - DevOps, Python

Job summary

Responsible for architecting, designing, and delivering machine learning operations solutions that integrate DevOps practices with scalable ML pipelines.

Qualifications

• Expert proficiency in ML Ops frameworks including MLflow, Kubeflow Pipelines, TFX, and Metaflow.
• Excellent skills in Python for ML pipeline development and automation.
• Expert knowledge of DevOps tools such as Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions.
• Advanced proficiency with infrastructure automation using Terraform and AWS CloudFormation.
• Excellent understanding of cloud platforms (AWS, Azure, GCP) for scalable ML deployments.
• Expert in monitoring and logging solutions including Prometheus, Grafana, ELK Stack, and Fluentd.
• Excellent command of version control systems: Git, GitHub, GitLab, Bitbucket.
• Strong scripting abilities in Bash and PowerShell for automation tasks.
• Solid experience in designing, implementing, and optimizing end-to-end ML pipelines.

Responsibilities

• Architect and implement ML Ops solutions using Python, MLflow, Kubeflow Pipelines, and TFX.
• Design and manage CI/CD pipelines for ML projects utilizing Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions.
• Develop infrastructure-as-code templates with Terraform and AWS CloudFormation.
• Integrate monitoring and logging solutions using Prometheus, Grafana, ELK Stack, and Fluentd.
• Lead the adoption of DevOps practices by configuring version control systems such as Git, GitHub, GitLab, and Bitbucket.
• Serve as a technical SME for ML Ops, providing guidance on best practices and tool selection.
• Mentor and train team members on ML Ops tools and automation strategies.
• Review and validate project deliverables for alignment with client specifications and quality standards.
• Recommend and implement client-focused value creation initiatives.

Skills

Relocation