ML Ops Technical Specialist - DevOps, Python

Sapsf; United States; On-site; USD 120,000 - 190,000; Full time

Job summary

Sapsf is seeking an ML Ops Technical Specialist in the United States to architect and implement ML Ops pipelines, automate model training and deployment, and monitor ML systems at scale.

You will design CI/CD pipelines, develop IaC templates, and integrate robust monitoring across cloud platforms. This role requires leadership in DevOps practices and mentoring team members on ML workflow optimization.

Qualifications

• Expert in ML Ops frameworks and tooling as listed.
• Strong Python automation for ML pipelines.
• Proficient in CI/CD orchestration and infrastructure as code.
• Experience with cloud-native ML deployments on AWS/Azure/GCP.
• Solid monitoring and logging across ML systems.

Responsibilities

• Architect and implement ML Ops solutions using Python, MLflow, Kubeflow Pipelines, and TFX.
• Design and manage CI/CD pipelines for ML projects.
• Develop IaC templates with Terraform and CloudFormation.
• Integrate monitoring with Prometheus, Grafana, ELK, Fluentd.
• Lead DevOps practices and version control strategies.
• Act as ML Ops SME and guide the team.
• Mentor team members on ML Ops tooling and cloud pipelines.
• Review project deliverables for client alignment and quality.
• Recommend client-focused value creation using ML Ops best practices.

Skills

ML Ops

Python

CI/CD tooling

Terraform

Cloud platforms

Monitoring & logging

Version control

Shell scripting

End-to-end ML pipelines

Education

Bachelor's degree

Tools

Jenkins

GitLab CI/CD

CircleCI

GitHub Actions

Terraform

AWS CloudFormation

Ansible

Prometheus

Grafana

ELK Stack

Fluentd

Job description

Career Opportunities: ML Ops Technical Specialist - DevOps, Python (178288)

Requisition ID178288-Posted10/08/2026

Key Responsibilities

• 1. Architect and implement ML Ops solutions using Python, MLflow, Kubeflow Pipelines, and TFX to automate model training, deployment, and monitoring processes.
• 2. Design and manage CI/CD pipelines for ML projects utilizing Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to ensure seamless integration and delivery of machine learning models.
• 3. Develop infrastructure-as-code templates with Terraform and AWS CloudFormation to provision and manage scalable cloud environments for ML workloads.
• 4. Integrate monitoring and logging solutions using Prometheus, Grafana, ELK Stack, and Fluentd to enable real-time performance tracking and issue resolution for ML systems.
• 5. Lead the adoption of DevOps practices by configuring version control systems such as Git, GitHub, GitLab, and Bitbucket for collaborative development and reproducibility.
• 6. Serve as a technical SME for ML Ops, providing guidance on best practices, tool selection, and workflow optimization within the team.
• 7. Mentor and train team members on ML Ops tools, automation strategies, and cloud-native ML pipeline development to build technical capability and mitigate delivery risks.
• 8. Review and validate project deliverables to ensure alignment with client specifications, quality standards, and industry benchmarks.
• 9. Recommend and implement client-focused value creation initiatives by leveraging advanced ML Ops frameworks and industry best practices.

Skill Requirements

• 1. Expert Proficiency In Ml Ops Frameworks Including Mlflow, Kubeflow Pipelines, Tfx, And Metaflow.
• 2. Excellent Skills In Python For Ml Pipeline Development And Automation.
• 3. Expert Knowledge Of Devops Tools Such As Jenkins, Gitlab Ci/Cd, Circleci, And Github Actions For Ci/Cd Orchestration.
• 4. Advanced Proficiency With Infrastructure Automation Using Terraform, Aws Cloudformation, And Ansible.
• 5. Excellent Understanding Of Cloud Platforms (Aws, Azure, Gcp) For Scalable Ml Deployments.
• 6. Expert In Monitoring And Logging Solutions Including Prometheus, Grafana, Elk Stack, And Fluentd.
• 7. Excellent Command Of Version Control Systems: Git, Github, Gitlab, Bitbucket.
• 8. Strong Scripting Abilities In Bash And Powershell For Automation Tasks.
• 9. Solid Experience In Designing, Implementing, And Optimizing Endtoend Ml Pipelines.

Other Requirements

• 1. Optional but valuable:
• 2. AWS Certified Machine Learning � Specialty
• 3. - Google Professional Machine Learning Engineer
• 4. - HashiCorp Certified: Terraform Associat