AI Architect III

Responsibilities

• Partner with business teams to understand operational challenges, define requirements, and identify AI opportunities that improve processes, decisions, productivity, or user experience.
• Review proposed AI use cases for business value, feasibility, data readiness, security, privacy, supportability, and alignment with enterprise needs.
• Design end-to-end AI solution architectures, including data flow, integrations, access controls, deployment approach, monitoring, and support requirements.
• Define reusable architecture patterns, design templates, standards, and decision criteria to support consistent AI solution delivery across the enterprise.
• Evaluate build, buy, or reuse options using Microsoft technologies, existing enterprise platforms, vendor solutions, internal tools, and emerging AI capabilities.
• Partner with security, infrastructure, data, compliance, application, and support teams to review risks, data sources, integrations, responsible AI controls, cost, scalability, and long-term maintainability.
• Guide implementation planning, testing, deployment, documentation, user readiness, operational support transition, monitoring, and continuous improvement for AI-enabled solutions.
• Maintain awareness of emerging AI technologies, platform capabilities, risks, regulations, and industry practices that may impact solution design.

• 5+ years of experience in IT architecture, solution design, application development, data platforms, automation, cloud platforms, or related technology roles.
• 1 to 2 years of hands‑on or architecture experience with generative AI, large language models, agents, automation platforms, and AI‑enabled business applications.
• Working knowledge of retrieval‑augmented generation, vector search, embeddings, prompt and context engineering, and LLM evaluation concepts.
• Strong understanding of data ecosystems, cloud platforms, APIs, integration patterns, identity and access controls, data classification, security, and responsible AI principles.
• Experience with enterprise architecture, solution architecture, application lifecycle practices, MLOps or LLMOps concepts, and production support models.
• Ability to translate business needs into technical requirements, document solution tradeoffs, and explain AI concepts in clear business terms.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Information Technology, Engineering, Business, or related field.

• Experience designing or implementing AI solutions with Microsoft technologies such as Azure Machine Learning, Azure OpenAI, Azure AI Foundry, Azure AI Search, Microsoft Fabric, Power Platform, Microsoft 365, or Dynamics 365.
• Experience establishing AI governance practices, including model lifecycle management, logging, auditability, risk review, and responsible AI controls.
• Experience evaluating vendor AI tools, enterprise platform capabilities, and third‑party solutions against business, technical, security, and cost requirements.
• Ability to balance business value, user adoption, cost, scalability, maintainability, and operational readiness when recommending AI solutions.
• Practical delivery mindset with experience moving AI solutions from discovery and design into implementation planning, support transition, and continuous improvement.

Equal Employment Opportunity/Veterans/Disability Employer