Preferred Location: Bangalore Total IT experience: 4 to 7 years in DWH is must 1 to 4 years of experience in taking ML models to production in large datasets Exposure to MLOps frameworks like MLFlow/ Neptune/anyother At least one Customer project of End to End Model deployment using Docker Containerization/Kubernetes 1-2 Projects involving deployment in Cloud like Azure/SageMaker/VertexAI etc and leveraging cloud native pipelines for Model Deployment Ability to perform Unit Testing on Batch Inference/Model Deployment using PyTest and other frameworks Ability to use Postman for API testing Configuring set-up and pipelines in Cloud preferable Azure/SageMaker/MLFlow Excellent grasp of Model Drift/Data Drift Terminologies Ability to handle multi-million requests for model inference Should have exposure to Git Version Control Should have built custom Model Monitoring Dashboards using Streamlit or cloud-native Model Monitoring Dashboards - SageMaker etc Basic understanding of ML models and NLP Good communication skills and interpersonal skills System Design of ML Applications Exposure to Jenkins/Azure Devops for CI/CD Pipelines