AI Domain Architect

Overview

Senior hands-on individual contributor role owning the domain intelligence layer of the AI Domain team: the ontology, agent workflow models, fine-tuning strategy, and intelligence pattern library that product teams build on.

What you'll do

• Design and own the canonical ontology as a semantic layer spanning all PrismHR platforms, anchored in the central PEO and HCM platforms, covering entities, relationships, business rules, and terminology for co-employment, payroll, benefits administration, workers' compensation, onboarding, and compliance, built in partnership with product teams who own each platform's data model.
• Ground the team's reference architectures in the ontology so retrieval, reasoning, and generated output stay consistent with how the business and its regulations 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.
• Model business processes across 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.
• Own the strategy for when to fine-tune, when to 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 data-handling constraints (retention, residency) required by the compliance posture.
• Build domain-specific evaluation harnesses that feed the team's model selection and evaluation process, measuring accuracy in PEO, HCM, and surrounding platform domains rather than generic benchmarks.
• 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 publishing it as a standard, and document when to use it, known failure modes, and evaluation criteria.
• Turn implementation lessons, pattern gaps, and production findings from product-team engagements into updated patterns and standards.
• Engage with product teams from design through go-live, reviewing designs, pairing on integration, and resolving pattern gaps, while the product team owns delivery and operations.
• Advise on use-case feasibility, risk, and fit against the standards, and 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.

What you'll need

• 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; ontology, patterns, and decision records are a real part of the output.

Nice to have

• 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.

Details

• Location: Noida, Uttar Pradesh, India (on-site).
• Hands-on senior individual contributor role; the team builds working proof-of-concepts as part of standard validation.