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–

📌 MLOps Architect With (Hyderabad)
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