ML Ops Technical Specialist - DevOps, Python

Maximum Salary (US): 172000

Minimum Salary (US): 87000

Job Summary This role is responsible for architecting, designing, and delivering robust machine learning operations solutions that integrate Dev Ops practices with scalable ML pipelines. The individual drives technical excellence, ensures adoption of best practices, and enables high-quality, automated workflows for model deployment, monitoring, and lifecycle management. They provide subject matter expertise, mentor team members, and champion the use of modern tools and cloud platforms to advance organizational objectives.

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, Git Lab CI/CD, CircleCI, and Git Hub Actions to ensure seamless integration and delivery of machine learning models.
3. Develop infrastructure-as-code templates with Terraform and AWS Cloud Formation 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 Dev Ops practices by configuring version control systems such as Git, Git Hub, Git Lab, 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 Cloud formation, 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 འSpecialty
3.

- Google Professional Machine Learning Engineer
4.

- Hashi Corp Certified:
Terraform Associat