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.