AI Domain Architect at Distro

You will join the AI Domain team, which owns the enterprise standards, reference architectures, approved model catalog, and governance safeguards for AI across the company. Within it 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.

Four areas of ownership. The canonical ontology as a semantic layer spanning all PrismHR platforms entities, relationships, business rules, and terminology across co-employment, payroll, benefits administration, workers' compensation, onboarding, and compliance built with the product teams who own each data model, and governed as a living artifact. Agent workflow modeling: business processes decomposed into agentic workflows with tool boundaries, decision points, escalation paths, and human-in-the-loop checkpoints, defining where agents may act autonomously and where they must defer, especially around payroll, money movement, and compliance. Fine-tuning strategy: when to fine-tune, when to retrieve, when prompting is enough, with dataset curation standards covering labeling, provenance, retention, and residency, and domain-specific evaluation harnesses that measure accuracy in PEO and HCM rather than on generic benchmarks. And the intelligence pattern library: retrieval strategies, reasoning templates, agent scaffolds, and validation guards, each proven by a working proof-of-concept before publication and documented with its failure modes.

You will engage with product teams from design through go-live, advise on use-case feasibility and risk, act as the escalation point for domain-AI design questions, and contribute to standards conformance decisions and recommendations to the AI Domain Committee.

You will need:

• 8+ 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
• 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 Microsoft Foundry: model deployment, agent development, and its evaluation and safety tooling
• Hands-on Azure more broadly 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