MLOps Architect With (Databricks)

Job Role: MLOps Architect Location : Hyderabad (Hybrid) Experience: 12–18 Years Role Objective We are looking for an experienced MLOps Architect to design and build enterprise-scale AI/ML platforms from the ground up. This role will define the end-to-end ML Operations architecture, enabling scalable, secure, governed, and production-ready ML platforms supporting 10,000+ ML models and 750+ trillion records. The ideal candidate will have deep expertise in Machine Learning Operations Architecture, Databricks, MLflow, Feature Stores, Model Governance, and Enterprise AI Platforms, with proven experience operationalizing large-scale ML workloads in production. Key Responsibilities Architect and build enterprise-scale MLOps platforms from scratch supporting the complete Machine Learning lifecycle. Define architecture for Feature Engineering, Feature Stores, Experiment Tracking, MLflow, Model Registry, Distributed Training, Hyperparameter Optimization, Model Deployment, Batch & Real-time Inference, Monitoring, Drift Detection, Explainability, AI Governance, Lineage, and Automated Retraining. Design scalable AI platforms capable of supporting thousands of production ML models and large-scale distributed AI workloads. Architect enterprise Lakehouse & AI platforms using Databricks, Unity Catalog, Delta Lake, Delta Live Tables (DLT), MLflow, and Mosaic AI. Define standards for Model Lifecycle Management, AI Governance, Responsible AI, Observability, Lineage, Security, and Compliance. Build scalable Feature Store and Model Serving architectures for batch, streaming, and real-time inference. Partner with Data Science, Data Engineering, and Enterprise Architecture teams to operationalize ML models into production. Drive platform scalability, reliability, performance, availability, and cost optimization. Mentor MLOps, ML, and Data Engineering teams while establishing enterprise architecture standards and best practices. Required Skills & Experience 12–18 years of experience in MLOps, Machine Learning Platform Engineering, AI Platform Architecture, or Data Engineering. Proven experience architecting and implementing enterprise MLOps platforms from scratch. Deep expertise in architecting the end-to-end ML lifecycle, including Feature Engineering, Feature Stores, Experiment Tracking, MLflow, Model Registry, Distributed Training, Hyperparameter Optimization, Model Deployment, Batch & Real-time Inference, Monitoring, Drift Detection, Explainability, AI Governance, Lineage, and Automated Retraining for enterprise-scale AI platforms. Strong expertise with Databricks, MLflow, Unity Catalog, Delta Lake, Delta Live Tables (DLT), Mosaic AI, Apache Spark, PySpark, Structured Streaming, and Lakehouse Architecture. Experience building large-scale AI platforms supporting thousands of production ML models and high-volume distributed data workloads. Strong programming skills in Python, PySpark, SQL, and distributed data processing. Experience working on Azure (Preferred), AWS, or GCP. Working knowledge of Kubernetes, Docker, Terraform, Linux, and cloud-native platforms to support scalable ML workloads. Good to Have Experience with GenAI, LLMOps, RAG, LangChain, LangGraph, Vector Databases, NVIDIA AI Stack, AI Agents, and modern AI frameworks. Experience building AI platforms supporting 10,000+ ML models, petabyte-scale data, or hyperscale enterprise workloads. Databricks, AWS, Azure, GCP, Kubernetes, or Terraform certifications are a plus.