Associate AI Engineer

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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.

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.

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

• 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
• 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
• Internal & external customer service
• Willingness to ask for context when requirements are incomplete
• Minimum 20 hrs of Continued Education (yearly)

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

Type Script, C# or another modern language

CI/CD, containerized services, monitoring, cost management or secure enterprise integration