ML Ops Technical Specialist - DevOps, Python

Job Summary

This role is responsible for architecting, designing, and delivering robust machine learning operations solutions that integrate DevOps 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.

Gen

AI Ops Engineer:

Role Summary:

Ensures reliable, scalable, and cost-efficient deployment and operations of GenAI applications.

Key Responsibilities:

• Manage CI/CD pipelines and automated deployments

• Containerization (Docker) and orchestration (Kubernetes)

• Implement AI observability (latency, tokens, errors)

• Optimize infrastructure and LLM cost

• Ensure high availability and incident management

• Manage security (secrets, access controls)

• Continuous monitoring and improvement

Skills:

• DevOps / MLOps expertise

• Azure cloud (preferred)

• CI/CD tools (Azure DevOps, GitHub Actions)

• Monitoring and observability tools

Success Metrics

• System uptime

• Cost efficiency

• Deployment frequency

• Incident resolution (MTTR)

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

• Google Professional Machine Learning Engineer

• Hashi Corp Certified: Terraform Associat