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. 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 Experience with real-time inference systems Exposure to monitoring tools (Prometheus, Grafana) Knowledge of LLM deployment / RAG pipelines