ML-Ops

Exp - only 5+ years will be considered

Build and maintain end to end ML pipelines including data preparation, model training, deployment, and monitoring.

Automate model deployment using CI/CD practices.

Manage model versioning, experiment tracking, and reproducibility.

Monitor model performance, data drift, and system reliability in production.

Collaborate with data scientists and data engineers to productionize models.

Implement governance, security, and access controls for ML workflows.

Optimize infrastructure usage and ensure scalable model serving.

Support incident troubleshooting and continuous improvement of ML systems.

Solid Python and SQL skills.

Experience with ML platforms such as Databricks, MLflow, SageMaker.

Knowledge of CI/CD tools and DevOps practices.

Experience with cloud platforms (AWS, Azure, or GCP).

Understanding of model lifecycle management and monitoring concepts.

Familiarity with containerization and orchestration (Docker, Kubernetes) is a plus.

Experience working with large-scale data platforms and distributed processing.

Exposure to feature stores, model governance, and automated retraining workflows.

📌 ML Ops (Bengaluru)
🏢 L T M
📍 Bengaluru