Job Title

Note: Looking for immediate joiners / candidates with notice period up to 15 days. What you need: Mandatory skills: Pyspark, MLFlow, MLOps, Model Lifecycle Management Orchestration (CI/CD). Strong experience in end-to-end MLOps lifecycle (deployment, monitoring, retraining). Hands-on with PySpark, Databricks, and large-scale data processing. Experience with CI/CD pipelines and ML deployment frameworks. Exposure to Docker and Kubernetes for containerized deployments. Familiarity with MLflow / model tracking and cloud platforms (AWS/Azure). What you would do: Design and maintain scalable ML pipelines for training, evaluation and deployment. Manage the end-to-end ML lifecycle using MLflow (experiments, registry, versioning). Productionise models with data scientists and ensure reliable deployment workflows. Build CI/CD pipelines for automated training, testing and release of models. Monitor model performance (drift, degradation) and implement retraining strategies. Manage cloud infrastructure and containerised workloads (Docker, Kubernetes) for scalable ML systems.