Associate AI Engineer

Pogue Construction•McKinney, TX

About The Position

The Associate AI Engineer is an early-career builder responsible for moving approved ideas from a governed backlog into reliable, well-documented solutions. Working as part of Pogue's AI production team, this individual develops, tests and supports AI and automation capabilities built on Pogue's Microsoft Fabric data platform, and contributes to the enterprise ontology and knowledge foundation that those solutions depend on. This is a hands-on development role with structured mentorship. Design review and pairing with Pogue's Principal Solutions Architect are a standing part of the work, and this individual is expected to partner directly with business owners so that what gets built is actually used.

Requirements

0-3 years' professional experience in software development, data engineering or a related technical role.
Bachelor's degree in computer science, software engineering, information systems, data science or a related field, or commensurate experience.
Team Player
Teachable
Curious & self-directed
Able to write & troubleshoot code in VS Code
Working knowledge of SQL
Working understanding of REST APIs, Git-based collaboration, testing & basic cloud concepts
Secure handling of credentials & data
Able to explain a technical project, the decisions personally made, the tradeoffs & how the result was validated
Clear written communication
Able to relate to & communicate with a diverse group of professionals
Ability to work individually & as part of a team
Self-motivated & driven
Highly organized & detail oriented
Highly analytical thinker
Positive Attitude
Internal & external customer service
Willingness to ask for context when requirements are incomplete

Nice To Haves

Microsoft Azure or Fabric, OneLake or lakehouse patterns, Power BI, Azure AI Search, Azure OpenAI or Azure AI Foundry LLM applications, RAG, embeddings, evaluation, prompt or model lifecycle management, agents or workflow automation
Coursework or project work in knowledge representation, semantic modeling, enterprise ontologies or knowledge graphs – including Protégé, OWL, RDF or SPARQL
Metadata, data lineage, document management or construction & project-control systems
TypeScript, C# or another modern language
CI/CD, containerized services, monitoring, cost management or secure enterprise integration

Responsibilities

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