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.