Solution Architect, Data Science and AI

Location

United States (Remote / Hybrid)

About the Role

The Solution Architect — Data Science & AI is a senior, client-facing technical leader responsible for designing and governing machine learning, advanced analytics, and generative AI solutions on Microsoft Azure and Databricks. This role demands deep expertise in data science, ML engineering, MLOps, and Gen AI — with enough architectural breadth across infrastructure, networking, and application development to participate credibly in cross-domain solution conversations. The ideal candidate is equally comfortable whiteboarding a model serving architecture during a presales pursuit as they are reviewing PySpark code, evaluating RAG retrieval quality, or designing an MLflow experiment tracking strategy.

This role spans the full engagement lifecycle, from presales architecture and deal shaping through delivery design assurance, ensuring that data science and AI solution intent is preserved from pursuit through implementation. The Solution Architect serves as a trusted technical authority to clients and internal teams alike, operating independently to lead workshops, shape solutions, and contribute to data strategy engagements alongside practice leadership.

Key Responsibilities

• 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.

Required Qualifications

• 8+ years in data science, ML engineering, or AI-f

***Travel up to 30-50%***