Job Summary (List Format):

- Design, build, and maintain scalable MLOps pipelines for end-to-end machine learning workflows.
- Lead containerization and orchestration of ML workloads using Kubernetes.
- Collaborate closely with data scientists and engineers to deploy and productionize ML models.
- Automate model lifecycle management including version control, rollback processes, and performance monitoring.
- Ensure ML systems are highly available, secure, and compliant with relevant standards.
- Develop and manage infrastructure as code using tools like Terraform or Helm.
- Establish and enforce best practices for model governance, reproducibility, and documentation.
- Utilize Python and Bash scripting for workflow automation and system integration.
- Leverage ML frameworks such as TensorFlow, PyTorch,



or Scikit-learn for model development and deployment.
- Work with cloud platforms including AWS, Azure, or GCP for scalable infrastructure.
- Implement CI/CD pipelines and monitoring systems for continuous integration and delivery.
- (Preferred) Work with MLOps tools like Kubeflow, MLflow, DVC, or LakeFS.
- (Preferred) Apply knowledge of model explainability and compliance frameworks.
- (Preferred) Contribute to open-source MLOps projects.
- Adhere to background verification requirements including employment verification, criminal record check, and proof of English fluency.

📌 MLOps Engineer (India)
🏢 Sparix Global
📍 India