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.

8+ years in data science, ML engineering, or AI-focused solution architecture roles.
Deep expertise in machine learning lifecycle — feature engineering, model training, evaluation, deployment, and monitoring.
Strong hands-on proficiency in Python and PySpark, with the ability to conduct code reviews and contribute to delivery when needed.
Strong expertise in Databricks, including MLflow, Model Serving, Unity Catalog, and Delta Lake.
Demonstrated experience designing and deploying generative AI solutions — RAG architectures, agentic patterns, fine-tuning, embeddings, document intelligence, evaluation frameworks, or computer vision.
Experience with Microsoft Fabric for analytics and data engineering workloads.
Familiarity with Azure AI services including Azure OpenAI Service, Azure AI Search, and Azure Machine Learning.
Proven client-facing consulting experience with the ability to independently lead presales and delivery engagements.
Strong presentation and architectural governance skills.
Demonstrated understanding of generative AI-assisted development tools and workflows, including GitHub Copilot, Claude Code, or spec-driven development practices, with the ability to apply these tools to accelerate architecture design, code generation, and delivery execution.

Solution Architect, Data Science and AI

Key skills