Description

You will lead the design and implementation of scalable MLOps infrastructure, bridging machine learning engineering with DevOps practices.

This role is on-site.

Responsibilities

• Implement and optimize ML pipelines using MLflow, Kubeflow Pipelines, TFX, and Metaflow for automated training and deployment.
• Automate infrastructure provisioning and management using Terraform, AWS CloudFormation, and Ansible to ensure scalable ML environments.
• Configure and maintain CI/CD workflows using Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline code integration.
• Monitor ML system performance and reliability using Prometheus, Grafana, ELK Stack, and Fluentd for rapid issue resolution.
• Lead technical reviews, evaluate architectural alternatives, and collaborate with stakeholders to define deliverables and mitigate risks.

Required Skills

• 5+ years of experience in MLOps, DevOps, or platform engineering.
• Advanced proficiency in Python for automation, scripting, and ML pipeline development.
• Strong expertise with container orchestration and pipeline tools: MLflow, Kubeflow, TFX, Metaflow.
• Hands-on experience with infrastructure as code: Terraform, AWS CloudFormation, Ansible.
• Deep knowledge of CI/CD platforms: Jenkins, GitLab CI/CD, CircleCI, GitHub Actions.
• Proficiency in monitoring and logging stacks: Prometheus, Grafana, ELK Stack, Fluentd.
• Expert-level version control skills with Git, GitHub, GitLab, and Bitbucket.
• Solid understanding of AWS cloud infrastructure and deployment strategies.

Preferred Skills

• Bachelor's degree in Computer Science or related field.
• 3+ years tenure in previous MLOps or DevOps roles.

Key Skills

Education

Bachelor's degree