AI Application Engineer

STACK Infrastructure•Denver, CO

9d•Hybrid

About The Position

We're building enterprise AI platforms that automate document-intensive review workflows across the business. The AI does the heavy lifting on document understanding, rule validation, and exception detection. The Product Engineer builds what users actually see and use — the reviewer workbench, the executive reporting layer, the data products, and the audit-ready outputs that turn AI insights into business decisions. This is a hybrid product engineering + analytics engineering role. You'll partner with the AI Solution Architect (who owns the agents) and the MLOps Engineer (who owns the data platform) to ship the user-facing layer. You report to the Head of AI & Data Strategy.

Requirements

5+ years building data-intensive applications, internal tools, or data products in production
Full-stack capability — modern web frameworks (React, Vue, or equivalent), Python or TypeScript, SQL
Production experience with at least one BI/reporting tool (Power BI preferred)
Strong SQL on Databricks SQL Warehouse, dbt, or equivalent lakehouse modeling
Proven UX intuition — you have shipped internal tools real users adopt and improved them based on observed behavior
Experience designing reporting consumed by executive stakeholders — balancing detail, accuracy, clarity
Microsoft 365 and Azure ecosystem familiarity (Power BI, SharePoint Online, Azure App Services, Entra ID)
Bachelor's degree in Computer Science, Engineering, or related field
Must be eligible to work in the United States
Must pass comprehensive background and drug screening

Nice To Haves

Power BI Data Analyst Associate or Databricks Data Analyst Associate certifications
Document-heavy UX experience — PDF viewers, annotation tools, evidence linking, comment logs
Experience surfacing LLM outputs to end users — confidence scoring, override flows, citation interfaces
React + TypeScript + modern frontend tooling and component libraries
Embedded analytics (Power BI embedded, Looker embedded, or equivalent)
Construction, real estate, data center, or AEC industry context
Experience operating in a small AI team where you wear multiple hats

Responsibilities

Reviewer Workbench & application engineering
Reviewer-facing application — triage incoming documents, accept or override AI recommendations, annotate decisions
UI/UX patterns that match how reviewers actually work today (comment logs, page-level evidence links, override flows)
Feedback capture — every reviewer interaction trains the model and refines the rule packs
Application performance at scale — handling high document volumes with thousands of pages of supporting context
Surfacing of agent outputs, confidence scores, and citations to reviewers in a way they trust
Reporting, dashboards & executive visibility
Power BI dashboards on Databricks SQL Warehouse — review throughput, accuracy, time saved, operational metrics
Executive reporting layers for SLT and business leadership
Portfolio analytics — cross-domain benchmarks, trend analysis, anomaly surfacing
Quarterly reporting infrastructure for SteerCo and AI Program Council reviews
Data products & analytics engineering
Clean, documented Gold-layer data products on Unity Catalog — reusable across business stakeholders, IT Delivery, AI team
Output generators — auto-produce Excel + Word + linked evidence outputs per reviewed item, audit-ready by default
APIs and integration patterns surfacing platform outputs to enterprise systems and business workflows
Analytics engineering standards — dbt or equivalent modeling, version control, lineage, documentation
User research & adoption
Direct work with business users — what's slowing them down, what's working, what they need next
Fast-cadence iteration based on real user feedback, not quarterly release cycles
User documentation, training materials, onboarding flows for end users and report consumers
Cross-functional partnership with the AI Solution Architect, MLOps Engineer, business stakeholders, IT Delivery, SLT