DevOps+MLOps+PythonML

About the Role

We are seeking skilled DevOps+MLOps+PythonML engineers who can bridge the gap between data science innovation and production-grade reliability. You will design robust CI/CD workflows, manage scalable ML pipelines, and ensure seamless deployment of Python-based models.

Key Responsibilities

• Platform & Automation: Design and maintain automated build, test, release, and deployment processes for ML services. Implement infrastructure automation to guarantee consistency across environments. Enhance system reliability through proactive monitoring and incident management.
  
• MLOps & Model Delivery: Build end-to-end ML pipelines ensuring reproducibility. Manage model versioning, artifacts, and controlled rollouts (canary/blue-green). Establish continuous feedback loops for performance monitoring and drift detection.

Collaboration & Engineering Excellence

• Partner with data science teams to productionize Python ML code using robust testing and optimization techniques.
  
• Define operational standards including logging, metrics, SLOs, and maintain comprehensive documentation.
  
• Actively participate in code reviews to improve security, scalability, and cost efficiency.

Minimum Qualifications

• Bachelor's degree (BTECH/MCA) or equivalent practical experience.
  
• 2–4 years of hands-on experience specifically in DevOps or MLOps engineering roles.
  
• Demonstrated expertise with CI/CD concepts and automation tools for deployments.

Preferred Qualifications

• Proven track record productionizing ML workflows end-to-end (from training to monitoring).
  
• Familiarity with containerization orchestration using Docker, Kubernetes.
  
• Experience with Infrastructure as Code tools such as Terraform or Ansible.
  
• Hands-on experience with MLOps tooling like MLflow, Kubeflow, and Airflow.
  
• Exposure to LLM-enabled applications including inference optimization.