AI Engineer (US)

Job Summary

Key Responsibilities

• 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.
• 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.
• 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 Infrastructureascode 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.
• Optional but valuable:
• AWS Certified DevOps Engineer
• Google Professional Machine Learning Enginee

Skill Requirements

Other Requirements