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STACK Infrastructure is seeking a Product Engineer to build the reviewer workbench, dashboards, and data products for enterprise AI platforms. You will partner with an AI Solution Architect and an MLOps Engineer to ship a user-facing layer, report automation, and audit-ready outputs for business decisions.
The role blends product engineering with analytics engineering, emphasizing user-centric design, fast iteration, and scalable data workflows.
STACK INFRASTRUCTURE (STACK) provides digital infrastructure to scale the world’s most innovative companies. We are an award-winning industry leader in building, owning, and operating highly efficient, cost-effective wholesale, colocation, and cloud data centers. Each of our national facilities meets or exceeds the highest industry standards in all operational categories of availability, security, connectivity, and physical resilience.
STACK offers the scale and geographic reach that rapidly growing hyperscale and enterprise companies need. The world runs on data. Data runs on STACK.
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. Responsibilities include but are not limited to:
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
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
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
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
$128,260.00 - $146,017.59
STACK is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity and expression, age, national origin, mental or physical disability, genetic information, veteran status, or any other status protected by federal, state, or local law