About this role
Job title: ML Ops Engineer
About the Role This role focuses on building, deploying, and maintaining robust machine learning pipelines and operational frameworks. The ideal candidate will apply ML Ops and DevOps practices to automate model training, validation, deployment, and lifecycle management, ensuring reliable delivery of ML solutions.
What You'll Do
- Implement and maintain ML pipelines using Python, MLflow, Kubeflow Pipelines, and TFX to automate model training, validation, and deployment processes.
- Apply DevOps practices with Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline CI/CD for machine learning workflows and monitor pipeline health.
- Utilize infrastructure-as-code tools such as Terraform and AWS CloudFormation to provision and manage scalable cloud resources for ML workloads.
- Integrate monitoring solutions like Prometheus, Grafana, ELK Stack, and Fluentd to track model performance, system metrics, and log analytics in production environments.
- Ensure process compliance by using Git, GitHub, GitLab, and Bitbucket for version control and code management within the team.
- Participate in technical discussions and feasibility studies to evaluate technical alternatives and support architecture best practices for ML Ops solutions.
- Prepare and submit status reports to highlight progress, minimize risks, and support project closure activities.
What We're Looking For
- Solid Proficiency In ML Ops, Including Automation Of ML Pipelines And Model Lifecycle Management.
- Solid Understanding Of DevOps Tools Such As Jenkins, GitLab CI/CD, CircleCI, And GitHub Actions For Workflow Automation.
- Solid Experience With Python For Scripting, Data Processing, And ML Pipeline Development.
- Solid Knowledge Of Infrastructure-as-Code Tools Like Terraform And AWS CloudFormation For Cloud Resource Management.
- Solid Skills In Monitoring And Logging Tools Including Prometheus, Grafana, ELK Stack, And Fluentd.
- Solid Familiarity With Version Control Systems Such As Git, GitHub, GitLab, And Bitbucket.
- Solid Ability To Participate In Technical Discussions And Support Process Compliance Within The Team.
Nice to Have
- AWS Certified DevOps Engineer
- Google Professional Machine Learning Engineer