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.