Practice Architect- AI​/ML

Azure Practice Architect (AI/ML)

The Azure Practice Architect (AI/ML) will serve as a senior technical leader at the intersection of enterprise data engineering and applied Generative AI. With a deep, career-long Microsoft data foundation, the 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. The role demands 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.

• Architect AI‑Ready Data Platforms:
  Design and lead the implementation of end‑to‑end data and AI architectures on Microsoft Fabric — spanning One Lake, 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.

• 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.
• Core Technical
  
  Skills:
  • Microsoft Fabric — One Lake, 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.
• Hands‑On Mindset:
  Proven ability to perform technical implementation, troubleshooting, and optimization across data and AI workloads in fast‑paced environments.
• Flexibility:
  Versatility in leading varied engagements — from AI proof‑of‑concepts and modernizations to enterprise‑scale intelligent platforms.
• Communication:
  …