- Design and implement CI/CD pipelines for ML workflows.
- Manage and optimize model training, validation, and deployment using tools like SageMaker , MLflow , or Kubeflow .
- Automate data pipelines and feature stores using AWS Glue , Airflow , or similar tools.
- Ensure model reproducibility , versioning , and monitoring in production.
- Collaborate with DevOps to integrate ML systems with existing infrastructure.
- Implement security , compliance , and governance best practices for ML systems.
- Solid experience with AWS services (SageMaker, IAM, Glue, CloudFormation).
- Proficiency in Python, Docker, and Git.
- Experience with CI/CD tools (GitHub Actions, Jenkins, etc.).
- Familiarity with monitoring tools (Prometheus, Grafana) and logging frameworks.
- Understanding of ML lifecycle, model drift, and data versioning.
Mandatory Skills
Python, MLOps
Vendor Proposed Rate
INR 7130 / day
Work Location
PAN India
Hybrid/remote/WFO
Hybrid
BGV Pre/Post onboarding
Post Onboarding
📌 Mlops (India)
🏢 Clifyx
📍 India
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