• Architect end-to-end machine learning and data science solutions on Azure and Databricks, from feature engineering through model serving and monitoring. • Design and govern MLOps pipelines using Databricks MLflow, Model Serving, and related tooling to enable repeatable, production-grade model deployment. • Architect generative AI solutions including Retrieval-Augmented Generation, agentic workflows, fine-tuning, document intelligence, embeddings, evaluation frameworks, and computer vision. • Contribute to modern data estate architectures using Microsoft Fabric and/or Azure Databricks, including lakehouse patterns and medallion architecture, to support ML and analytics workloads. • Contribute to client data strategy engagements including maturity assessments, analytics roadmaps, and governance frameworks in partnership with practice leadership. • Independently lead client-facing presales engagements, discovery workshops, and architecture reviews as the primary technical authority on data science and AI pursuits. • Develop level-of-effort estimates, architecture deliverables, and technical contributions to Statements of Work for client pursuits. • Provide delivery design assurance and architectural governance across active data science and AI engagements, including code reviews of Python, PySpark, and ML model implementations. • Design cloud-native Azure architectures using CAF and Well-Architected Framework principles as they apply to AI and data workloads.