AI Domain architect 6916

We are looking for an experienced AI Domain Architect to help design and build the domain intelligence layer behind enterprise AI solutions.

? Role Description

As an AI Domain Architect , you will be responsible for defining how AI systems understand and reason about complex business domains. You will work across domain modeling, ontologies, AI agents, RAG, fine-tuning, model evaluation, and reusable AI patterns , partnering closely with product and engineering teams.

This is a Senior Individual Contributor, hands-on role for someone who combines strong architectural thinking with practical AI engineering skills. You will take ideas from concept to working Proof of Concept (PoC) and ultimately turn proven approaches into scalable enterprise standards.

?️ Responsibilities

• Design and evolve ontologies, knowledge graphs, semantic layers, and canonical domain models for enterprise platforms.
• Define entities, relationships, business rules, and terminology to improve AI retrieval, reasoning, and generated outputs.
• Design agentic AI workflows , including task decomposition, tool boundaries, decision points, escalation paths, and human-in-the-loop checkpoints.
• Define where AI agents can operate autonomously and where human intervention is required, particularly in sensitive or regulated processes.
• Establish strategies for when to use RAG, fine-tuning, or prompting based on accuracy, cost, scalability, and maintainability.
• Define standards for AI dataset preparation, labeling, provenance, governance, retention, and data-handling requirements.
• Build domain-specific model evaluation pipelines and frameworks .
• Develop reusable AI patterns, including retrieval strategies, reasoning templates, agent scaffolds, and validation guards.
• Build Proofs of Concept (PoCs) to validate architectures and patterns before establishing them as standards.
• Work hands-on with Microsoft Foundry and Azure for model deployment, agent development, evaluation, and AI safety.
• Partner with product and engineering teams from solution design through go-live.
• Create technical standards, architecture documentation, patterns, and decision records.
• Serve as a technical reference and escalation point for AI architecture and domain intelligence decisions.

Requisitos

• 8+ years of professional software engineering experience in production environments.
• Experience at Staff Engineer, Principal Engineer, Architect, or equivalent senior technical level .
• Hands-on experience designing ontologies, knowledge graphs, semantic layers, or canonical domain models deployed in production.
• Practical experience building solutions with LLMs and/or agentic AI systems .
• Experience with RAG, fine-tuning, and model evaluation pipelines , with strong judgment on when to use each approach.
• Hands-on experience with Microsoft Foundry , including model deployment, agent development, evaluation, and safety tooling.
• Strong hands-on experience with Microsoft Azure , including compute, data, identity, and networking services supporting AI workloads.
• Ability to take an AI pattern or architecture from concept → Proof of Concept → published standard .
• Experience defining architectures, technical patterns, or standards used across multiple teams.
• Proven ability to influence technical direction across teams without direct authority.
• Excellent written and verbal communication skills.
• Advanced English proficiency .

Nice to have:

• Experience with PEO, HCM, payroll, benefits , or other regulated business domains.
• Knowledge of OWL, RDF, SHACL, property graphs , or other knowledge-representation technologies.
• Experience with multi-tenant SaaS platforms handling sensitive data.
• Experience working in SOC 2 or ISO 27001 environments.
• Familiarity with Microsoft Agent Framework, MCP (Model Context Protocol), and tool-use orchestration .
• Knowledge of Snowflake Cortex AI and its integration within broader AI platforms.

? We’re looking for a senior professional who combines AI Engineering, Architecture, and Domain Modeling and can turn complex business knowledge into scalable, production-ready AI capabilities.