Practice Architect- AI/ML

TEKsystems•Berwyn, IL

1d•$136,000 - $204,000•Remote

About The Position

The Azure Practice Architect (AI/ML) will serve as a senior technical leader at the intersection of enterprise data engineering and applied Generative AI. Building on a deep, career-long Microsoft data foundation. Practice Architect will design, architect, and deliver intelligent, production-grade solutions spanning Microsoft Fabric, Microsoft Foundry, Azure AI Studio, Microsoft Copilot, and modern Gen AI patterns. With a hands-on approach, he/she will lead the transformation of data platforms into AI-ready ecosystems — combining robust data modeling, engineering, and Power BI analytics with retrieval-augmented generation, agentic workflows, and LLM-powered applications. This role demands the versatility to lead diverse engagements, from AI proof-of-concepts to enterprise-scale intelligent platforms, while fostering innovation, responsible AI practices, and measurable client success.

Requirements

Experience: 12+ years of hands-on data engineering experience, with a Microsoft data technology background sustained throughout your career and demonstrable delivery of AI/ML and Gen AI solutions on Azure.
Microsoft Fabric — OneLake, lakehouse, data engineering, real-time analytics, and Fabric Data Agents.
Microsoft Foundry and Azure AI Studio — building, evaluating, and deploying Gen AI applications and agents.
Microsoft Copilot — designing, extending, and operationalizing copilot and agentic experiences.
Generative AI — RAG, prompt engineering, embeddings/vector search, model selection, fine-tuning, and evaluation.
Power BI — semantic modeling, DAX, Power Query, Copilot for Power BI, and integration with Fabric.
Data Modeling & Data Engineering — dimensional modeling, medallion architecture, ETL/ELT, and pipeline orchestration using Azure-native tools.
Data Governance and data quality using Microsoft Purview ecosystem.
Proven ability to perform technical implementation, troubleshooting, and optimization across data and AI workloads in fast-paced environments.
Versatility in leading varied engagements — from AI proof-of-concepts and modernizations to enterprise-scale intelligent platforms.
Excellent verbal and written communication skills, with the ability to articulate complex AI and data concepts to technical and non-technical audiences and to facilitate workshops.

Responsibilities

Architect AI-Ready Data Platforms: Design and lead the implementation of end-to-end data and AI architectures on Microsoft Fabric — spanning OneLake, lakehouse, data engineering, Microsoft Purview and Fabric Data Agents — engineered to serve as the trusted foundation for Gen AI and analytics workloads.
Generative AI Solution Design: Architect and deliver Gen AI solutions using Microsoft Foundry and Azure AI Studio, including RAG pipelines, prompt engineering, model selection and evaluation, grounding on enterprise data, fine-tuning, and safe, scalable deployment of LLM-powered applications. Operationalize Microsoft Copilot experiences and custom copilots/agents, integrating enterprise data sources.
Data Engineering & Modeling Excellence: Build and optimize enterprise-grade data pipelines and semantic models — applying best practices in dimensional modeling, medallion architecture, ETL/ELT, and data quality — to make data reliable, governed, and consumable by AI systems.
Power BI & Intelligent Analytics: Develop and optimize interactive Power BI dashboards and semantic models, embedding Copilot and AI-driven insights, natural-language querying, and real-time reporting over large-scale datasets.
Hands-On Delivery: Actively contribute to code development, notebook and pipeline authoring, prompt and agent development, evaluation, and deployment across Azure environments — rolling up your sleeves to unblock teams and keep engagements moving.
Engagement Leadership: Drive AI/ML and data engagements end-to-end — from discovery, use-case shaping, and solution scoping through deployment, optimization, and knowledge transfer — adapting to diverse client needs and evolving Microsoft AI capabilities.
Collaboration & Innovation: Partner with data engineers, data scientists, business stakeholders, and Microsoft field teams to align AI solutions with business value; stay ahead of the Microsoft AI roadmap to recommend forward-thinking, differentiated architectures.
Mentorship & Best Practices: Guide and upskill team members on Fabric, Gen AI, and agentic patterns; conduct design and code reviews; and champion reusable accelerators, reference architectures, and delivery standards.