---
title: 'AI Domain Architect at Distro'
canonical: 'https://feeny.ai/job/ai-domain-architect-distro-multiple-locations-hqag136btj61'
type: 'job'
last_seen: '2026-10-27'
---

# AI Domain Architect at Distro

- **Company:** Distro
- **Location:** Multiple Locations
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-10-26
- **Last confirmed live:** 2026-10-27
- **Apply:** https://jobs.lever.co/distro/45680353-c75a-4a71-a815-c1dc8e603ae4

## Job description

We are hiring the architect who teaches AI what our business actually means. You will be a member of the AI Domain team, the group that owns the enterprise standards, reference architectures, approved model catalog, and governance safeguards for AI across the company. Within that team, you own the domain intelligence layer: the ontology, the agent workflow models, the fine-tuning strategy, and the intelligence pattern library that product teams build on. This is a hands-on senior individual contributor role. Patterns here are proven through working proof-of-concepts before they become standards, so you will build as much as you write. Responsibilities Platform ontology design • Design and own the canonical ontology as a semantic layer spanning all PrismHR platforms, anchored in the central PEO and HCM platforms — entities, relationships, business rules, and terminology covering co-employment, payroll, benefits administration, workers’ compensation, onboarding, and compliance — built in partnership with the product teams who own each platform’s data model • Ground the team’s reference architectures in that ontology so retrieval, reasoning, and generated output stay consistent with how the business and its regulations actually work • Establish governance for ontology evolution as products, regulations, and customer needs change, and maintain it as a living artifact with a defined review cadence AI agent workflow modeling • Model business processes across the PrismHR platforms — PEO and HCM foremost — as agentic workflows: task decomposition, tool boundaries, decision points, escalation paths, and human-in-the-loop checkpoints mapped to the team’s risk-level requirements • Define where agents may act autonomously and where they must defer, especially in payroll, money movement, and compliance-sensitive operations • Ensure workflow models inherit the team’s embedded safeguards — validated identity claims for tenant context, audit logging, content safety — rather than reinventing them per use case AI fine-tuning strategy • Own the strategy for when we fine-tune, when we use retrieval, and when prompting is enough, based on cost, accuracy, and maintenance trade-offs • Define dataset curation standards for domain-specific training data, including labeling, provenance, and the data-handling constraints (retention, residency) our compliance posture requires • Build domain-specific evaluation harnesses that feed the team’s model selection and evaluation process, measuring accuracy in PEO, HCM, and the surrounding platform domains rather than generic benchmarks AI intelligence pattern library • Build and maintain the reusable library of AI intelligence patterns — retrieval strategies, reasoning templates, agent scaffolds, validation guards — so product teams start from something proven • Validate each pattern through a working proof-of-concept before it is published as a standard, and document when to use it, known failure modes, and evaluation criteria • Run the feedback obligation: turn implementation lessons, pattern gaps, and production findings from product-team engagements into updated patterns and standards Partnership • Engage with product teams from design through go-live — reviewing designs, pairing on integration, resolving pattern gaps — while the product team owns delivery and operations • Advise on use-case feasibility, risk, and fit against the standards; act as the escalation point for domain-AI design questions • Contribute to the team’s decisions on standards conformance and its recommendations to the AI Domain Committee Requirements • We are hiring for domain modeling judgment and practical AI engineering. Deep expertise in both is rare; strength in one and fluency in the other works. • Eight or more years building production software, including time at staff, principal, or architect scope where other teams depended on what you owned • Practical experience designing ontologies, knowledge graphs, or canonical domain models that shipped in production systems • Hands-on experience with LLM-based or agentic systems: you have built with them and know where they break • Experience with fine-tuning, RAG, or model evaluation pipelines, and clear judgment about which to reach for • Hands-on experience with Microsoft Foundry services, including model deployment, agent development, and its evaluation and safety tooling • Hands-on experience with Azure services more broadly, including the compute, data, identity, and networking building blocks AI workloads run on • Ability to take a pattern from concept through working proof-of-concept to published standard • A record of changing technical direction through influence rather than authority, across team boundaries • Clear writing. The ontology, patterns, and decision records are a real part of the output. Preferred • PEO, HCM, payroll, benefits, or adjacent regulated-domain experience • Working knowledge of ontology and knowledge-representation tooling (OWL, RDF, SHACL, property graphs) or semantic layer design • Experience with multi-tenant SaaS handling sensitive personal data, including SOC 2 or ISO 27001 environments • Familiarity with agent frameworks — Microsoft Agent Framework in particular — MCP, and tool-use orchestration • Knowledge of Snowflake Cortex AI and how it fits alongside a broader AI platform • Prior AI research experience and formal knowledge-engineering credentials are optional. Domain curiosity is not.

#VensureHRIndia

## #LI-PROMOTED

#LI-Onsite

## About Distro

## Company Overview
- **One-liner**: Distro provides an AI-powered global hiring platform that automates candidate screening, outreach, scheduling, and payroll compliance for remote-first companies.
- **Entity Type**: Private (Acquired by Vensure Employer Services)
- **Headquarters**: Lehi, Utah, United States
- **Founded**: 2021
- **Founders**: Chad Ingram (CEO), Andrew Mackay (Co-Founder)

## Core Business
- **Primary industry**: Recruitment / Talent Acquisition Technology
- **Target customers**: B2B – seed-stage startups to enterprise teams, focused on remote-first and globally distributed companies
- **Mission or purpose statement**: “Hiring should feel human. Not heavy.”

## Products & Services
- **AI Recruiting Assistant**: Automates resume screening, scoring, and candidate ranking based on job criteria; handles outreach and scheduling end-to-end.
- **AI Scheduling Assistant**: Automates interview scheduling, reducing one of the biggest administrative burdens in recruiting.
- **Global Hiring Marketplace**: Matches companies with top technical talent across 200+ countries, including payroll and compliance support in 90+ EOR (Employer of Record) countries.

## Market Standing
- **Valuation / Market Cap**: Not disclosed
- **Key Metric**: $2M ARR reached within 8 months of MVP (reported as a milestone); current total funding not publicly available
- **Notable Investors / Partners**: Acquired by Vensure Employer Services (January 2026); serves companies from seed-stage to enterprise
- **Growth Signals**: 26.3% headcount growth YoY (19 employees as of mid-2026), 222% LinkedIn follower growth YoY, 4900% increase in job postings YoY (100 active positions), SOC 2 Type I certified, 1–3 days to shortlist

## Competitive Advantages
- AI-first screening reduces recruiter workload by surfacing only the top 3–5 candidates
- Global reach (200+ countries, 90+ EOR countries) with full compliance handling
- Skills-based screening (not pedigree) – appeals to a broader, more diverse talent pool
- Fast time-to-shortlist (1–3 days) and automated scheduling reduce time-to-hire by 1–2 weeks

## Strategic Focus
- Continue expanding AI capabilities in recruiting (e.g., AI Scheduling Assistant)
- Deepen integration with Vensure Employer Services to scale global payroll and compliance
- Grow the marketplace by attracting more technical talent and enterprise clients
- Maintain SOC 2 compliance and enterprise-grade security standards

## Why Work Here
- **Culture highlights**: AI-first, mission-driven (“make hiring human”), global team spanning 8 countries, emphasis on fair and skills-based screening
- **Remote/hybrid/office policy**: Some roles are on-site (Lehi, UT; Chandler, AZ) while the company supports remote employees across multiple countries; hybrid/remote likely role-dependent
- **Notable perks or engineering culture**: Fast-paced startup environment with recent acquisition support; employees work on cutting-edge AI recruiting tools; dedicated account managers for candidates and a focus on long-term roles

## Sources
1. [distro.io](https://distro.io/)
2. [distro.io/about-us](https://distro.io/about-us)
3. [distro.io/press](https://distro.io/press)
4. [linkedin.com/company/distroio](https://www.linkedin.com/company/distroio)
5. [jobs.lever.co/distro](https://jobs.lever.co/distro)

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