Principal MLOps Platform Engineer

As a Principal MLOps Platform Engineer, you will:

• Lead the evolution of enterprise Machine Learning and AI platforms
• Design scalable platform architecture for AI Agents and Vector Database implementations
• Evaluate and operationalize new platform capabilities and emerging Databricks features
• Establish engineering standards and governance for production-grade GenAI applications
• Design and manage Feature Stores, Model Registries, and Vector Search infrastructure
• Enable Retrieval Augmented Generation workflows across enterprise AI use cases
• Mentor senior engineering teams on platform design patterns, code quality, and architecture
• Translate AI and ML engineering requirements into robust engineering roadmaps
• Drive automation, scalability, and operational excellence across AI infrastructure
• Collaborate with cross-functional teams to enable production-ready AI solutions

• 10+ years of experience across Data Engineering, MLOps, and AI Infrastructure
• Deep expertise with Databricks AI Stack including MLflow, Mosaic AI, Unity Catalog, Feature Store, Model Serving, and Vector Databases
• Advanced knowledge of Apache Spark internals, structured streaming, and performance optimization
• Strong experience with AWS infrastructure including EC2 and GPU-based compute environments
• Expert-level Python programming skills with hands-on PyTorch experience
• Strong experience with Terraform and Terragrunt for infrastructure automation
• Hands-on expertise with Docker and Kubernetes
• Experience building CI/CD pipelines using Git Hub Actions
• Strong observability and monitoring experience using Datadog
• Proven experience operationalizing traditional ML and Generative AI workloads

• Architect and scale enterprise-grade ML and AI platforms
• Optimize distributed compute workloads for large-scale processing
• Build low-latency, high-concurrency model serving systems
• Bridge experimental ML development with production-grade engineering
• Drive technical leadership and mentor engineering teams
• Translate business and AI requirements into scalable technical solutions

• Opportunity to build cutting-edge enterprise AI and GenAI platforms
• Exposure to advanced Databricks, MLOps, and cloud-native technologies
• A highly collaborative and innovation-driven engineering environment
• Opportunities to shape enterprise AI strategy and technical standards