Associate AI Engineer

Pogue Construction is building an AI production team that develops and supports bounded AI and automation on a governed Microsoft Fabric data platform. This early-career Associate AI Engineer role focuses on turning approved backlog items into reliable releases, partnering with business owners to drive adoption, and ensuring solutions are maintainable, measured, and safe to operate on day one.

Based in McKinney, TX with onsite expectations, the Associate AI Engineer will work across lakehouse data, enterprise semantics, and secure retrieval patterns to deliver AI-enabled capabilities that respect permissions, citations, versions, and effective dates.

What you’ll do

• Develop, test, and improve bounded AI and automation capabilities using VS Code, SQL, APIs, Git, and Pogue-approved cloud services
• Support solutions including permission-aware enterprise search, retrieval-augmented generation (RAG), internal assistants, workflow support, and AI-enabled analytics
• Convert approved backlog items into requirements, technical tasks, acceptance criteria, and small releases that can be measured and safely supported
• Create prototypes during discovery, then help convert validated prototypes into maintainable services and reusable patterns
• Join design review with the Principal Solutions Architect before development starts, then carry approved designs through release
• Work with Microsoft Fabric lakehouse data, governed Gold business objects, semantic models, metadata, and shared business definitions
• Connect structured data and approved documents through secure APIs and retrieval patterns while preserving source permissions, citations, versions, and effective dates
• Contribute to Pogue’s enterprise ontology and knowledge foundation by documenting objects, relationships, definitions, ownership, and authoritative sources
• Build only on data confirmed as validated in the Gold layer
• Document architecture, assumptions, decisions, data flows, interfaces, tests, deployment steps, and operating runbooks for support handoff
• Evaluate accuracy, failure modes, access behavior, latency, cost, and usefulness, then convert results into release recommendations
• Follow Pogue standards for identity, role-based access, data-classification, approved-model usage, logging, monitoring, and human-approval requirements
• Surface uncertainty, security concerns, and data-quality issues early, proposing practical options instead of hiding risk
• Work directly with business owners and subject-matter experts to understand the decision or workflow before selecting an approach
• Explain tradeoffs in plain language, ask focused questions, demonstrate progress, and incorporate feedback from technical and nontechnical teammates
• Support adoption through concise guides, examples, training materials, and responsive follow-through after launch
• Support the AI Champions Network as solutions roll out to project teams

Required qualifications

• Team player
• Teachable
• Curious and self-directed
• Ability to write and troubleshoot code in VS Code
• Working knowledge of SQL
• Working understanding of REST APIs, Git-based collaboration, testing, and basic cloud concepts
• Secure handling of credentials and data
• Ability to explain a technical project, including decisions made, tradeoffs, and how the result was validated
• Clear written communication
• Ability to relate to and communicate with a diverse group of professionals
• Ability to work individually and as part of a team
• Self-motivated and driven
• Highly organized and detail oriented
• Highly analytical thinker
• Positive attitude
• Internal and external customer service
• Willingness to ask for context when requirements are incomplete
• Minimum 20 hrs of Continued Education (yearly)

Education

Bachelor’s degree in computer science, software engineering, information systems, data science, or a related field (or equivalent training) is required.

Technologies

• VS Code, SQL, APIs, Git
• Microsoft Fabric, OneLake
• Azure AI Search, Azure OpenAI, Azure AI Foundry
• Power BI
• LLM applications, RAG, embeddings
• Prompt or model lifecycle management
• Agents or workflow automation
• Protégé, OWL, RDF, SPARQL
• TypeScript, C#, CI/CD, containerized services, monitoring