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AI Domain Architect

Distro
USA
Full-time
Mid
Onsite
Discovered Today
Azure

Full Job Posting

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
  • Additional information

Preferred Qualifications

  • Preferred
  • Prior AI research experience and formal knowledge-engineering credentials are optional. Domain curiosity is not.

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