Experience- 5+ Years We are looking for an experienced MLOps / AI Ops Engineer to design build and manage enterprise AI/ML platforms. The ideal candidate should have hands-on experience in MLOps AI Platform Engineering Feature Stores Model Registry/Catalog AI Governance Kubernetes Azure and production AI deployments. Key Responsibilities Design and maintain enterprise AI/ML platforms. Build and manage Feature Stores and Model Registry/Catalog. Develop scalable MLOps pipelines for model training deployment and monitoring. Implement AI Governance security compliance and observability. Deploy AI/ML workloads using Kubernetes and Docker. Build and manage Agentic AI platform infrastructure and AI Agent Catalog. Collaborate with Data Scientists Software Engineers and DevOps teams. Monitor production AI systems and ensure high availability. Required Skills 5+ years of experience in MLOps AI Platform Engineering or AI Ops. Strong experience with Feature Stores (Feast Databricks Feature Store Azure ML Feature Store). Experience with Model Registry/Catalog (MLflow Azure ML Registry SageMaker Model Registry). Hands-on experience with MLOps tools (Kubeflow MLflow Airflow GitHub Actions Jenkins). Experience with Kubernetes Docker Python and REST APIs. Knowledge of Azure Cloud (preferred). Experience with AI Governance model monitoring security and observability. Exposure to LLM platforms and Agentic AI is an added advantage. Excellent communication and problem-solving skills. Preferred Qualifications Experience designing enterprise AI platforms. Experience supporting production AI/ML workloads. Knowledge of CI/CD for machine learning pipelines. Pay From ₹800000.00 per year Application Question(s) How many years of hands-on experience do you have in MLOps or AI Platform Engineering? Have you worked on enterprise AI/ML platform architecture? Have you implemented or managed a Feature Store in a production environment? Which Feature Store(s) have you worked with? Have you worked with Model Registry or Model Catalog solutions (e.g. MLflow Azure ML Registry SageMaker Model Registry)? Which cloud platform have you primarily worked on? If selected how soon can you join (Days)? Notice Period CCTC and ECTC? Work Location Remote