Job Description: We are looking for a hands on MLOps Engineer who will work closely with Data Scientists and ML Engineers to build deploy monitor and optimize machine learning models in production This role is NOT focused on platform engineering or infrastructure only work instead it emphasizes end to end ML lifecycle management model deployment and operationalization of ML systems Key Responsibilities: Strong Python programming Hands on experience in Machine Learning workflows Experience with MLOps tools MLflow Kubeflow Airflow SageMaker Vertex AI Knowledge of CI CD tools GitHub Actions Jenkins etc Experience deploying models using Docker REST APIs Flask FastAPI Understanding of Model versioning Experiment tracking Model monitoring Technical Requirements: Exposure to cloud platforms AWS Azure GCP for ML deployment Knowledge of LLMOps GenAI deployment pipelines Familiarity with Feature stores Data pipelines Spark Kafka Experience with Kubernetes basic deployment level Additional Responsibilities: Experience with real time inference systems Exposure to monitoring tools Prometheus Grafana Knowledge of LLM deployment RAG pipelines Preferred Skills: Analytics->Data Science,Technology->Machine learning->data science,Technology->Machine Learning->Generative AI