Job Summary (List Format):

- Architect and maintain scalable MLOps pipelines for model training, deployment, and monitoring.
- Lead implementation of containerized ML workloads using Kubernetes.
- Collaborate with data scientists and engineers to productionize ML models.
- Automate model lifecycle management, including versioning, rollback, and performance tracking.
- Ensure high availability, security, and compliance of ML systems.
- Develop infrastructure as code using tools such as Terraform or Helm.
- Establish and enforce best practices for model governance and reproducibility.
- Requires a Bachelor's degree in Computer Science, Engineering, or related field (Master's preferred).
- 5–10 years of experience in MLOps, DevOps, or software engineering.




- Extensive experience with Kubernetes and container orchestration.
- Proficient in Python and Bash scripting.
- Experience with ML frameworks (TensorFlow, PyTorch, or Scikit-learn).
- Familiarity with cloud platforms (AWS, Azure, GCP).
- Knowledge of CI/CD tools and monitoring systems.
- Preferred: Experience with Kubeflow, MLflow, data versioning tools (DVC, LakeFS), and model compliance frameworks.
- English fluency required for international collaboration.
- Background verification process includes employment check and police clearance certificate.
- Candidate should be an Inhouse Bench resource.

📌 MLOps Engineer (India)
🏢 Sparix Global
📍 India