Job DescriptionThe AI Domain Architect is a senior enterprise architecture role responsible for designing and operationalizing AI-enabled solutions across assigned domains and delivery portfolios.
This role sits at the point where enterprise AI strategy becomes working software. The AI Domain Architect translates reference architectures, governance requirements, and platform capabilities into production-ready solution designs, and then stays in the work alongside engineering to see those designs through to delivery.
Operating at the enterprise level, the AI Domain Architect influences architectural direction across multiple initiatives while partnering closely with engineering, product, governance, and platform stakeholders to ensure AI systems are secure, observable, cost-effective, and aligned with AGS standards. This is not a purely advisory role. The AI Domain Architect works directly with the AI Product, Engineering, and delivery teams to ensure designs move successfully from concept to production.
ResponsibilitiesSolution Architecture for AI-Enabled Systems- Design AI-enabled architectures across assigned domains, aligned with enterprise standards, platform capabilities, and AGS reference patterns
- Translate enterprise reference architectures into domain-level implementation blueprints that engineering teams can execute against
- Guide design decisions for agentic systems, orchestration workflows, retrieval and grounding patterns (RAG), model integration, and tool use
- Architect secure integration patterns covering identity, permissions, auditability, and service-to-service authentication for AI workloads
- Partner with engineering leads to ensure designs are scalable, maintainable, and realistic for the team's context
Production Readiness and Operationalization- Embed observability, telemetry, evaluation, and monitoring requirements into solution designs from day one
- Build in lifecycle management, model and prompt versioning, cost monitoring, and safe deployment practices
- Define evaluation approaches including baseline test sets, regression coverage, quality thresholds, and human-in-the-loop checkpoints where appropriate
- Integrate logging, safety signals, and performance metrics in partnership with AI Product, Engineering, and Delivery teams
- Support production readiness reviews and architectural risk assessments before go-live
Governance, Risk, and Responsible AI- Translate governance and risk requirements into practical architectural controls that don't slow delivery unnecessarily
- Ensure required documentation, evidence, and compliance checkpoints are built into delivery workflows rather than bolted on at the end
- Guide teams through architecture reviews and governance intake, including the judgment calls that sit between them
- Embed approved guardrails, content safety controls, and platform policies into solution designs
- Proactively surface architectural and AI-specific risks (data leakage, prompt injection, model misuse, cost exposure) and propose mitigations
Patterns, Reuse, and Platform Feedback- Promote reuse of approved templates, patterns, and reference implementations across the domain
- Identify duplication and drift within the domain and recommend consolidation
- Provide structured feedback to enterprise architecture and platform teams so reusable assets keep getting better
- Contribute to evolving standards based on implementation learnings and post-production insights
Delivery Partnership and Technical Leadership- Operate as a trusted technical partner to engineering, product, governance, and domain leadership
- Participate in technical design workshops, delivery planning, and architectural due diligence for AI-enabled integrations and third-party solutions
- Support roadmap planning by bringing architectural feasibility, scalability, and total-cost considerations into the conversation early
How You Work- Partner, not gatekeeper. You earn the right to be heard by being in the work, not by standing outside it.
- Accelerator, with judgment. You speed teams up when you can, and slow them down when you should. You know the difference, and you can explain it.
- Hands-on when it helps. You stay current enough to prototype, pair, and debug alongside engineers, even though shipping the final code is the team's job, not yours.
- Enterprise-minded, delivery-oriented. You hold the bigger picture without losing sight of what's actually shipping.
Qualifications- Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent experience
- 8 to 12+ years in enterprise architecture, solution architecture, platform engineering, or AI-enabled system design
- Demonstrated experience designing and delivering production-grade AI or automation solutions, not just proofs of concept
- Proven experience designing enterprise-scale Azure architectures aligned to the Azure Well-Architected Framework, with particular strength across security, cost management, and governance
- Deep working knowledge of identity and access architecture on Azure, including Microsoft Entra ID, RBAC, managed identities, service principals and workload identities, and network isolation patterns (private endpoints, VNet integration), applied to AI workload patterns
- Expertise designing and deploying solutions on Microsoft Foundry (formerly Azure AI Foundry / Azure AI Studio) and Azure OpenAI Service, including model lifecycle management, evaluation tooling, prompt flow, Content Safety, and integration with enterprise applications
- Strong command of AI system design patterns: agentic orchestration, RAG and grounding architectures, tool use, evaluation strategies, and operational considerations for production AI
- Strong understanding of Model Context Protocol (MCP), both as server publisher (tool registration, schema design, transport modes, capability negotiation) and as client consumer (approval, authentication, and governance of MCP tools)
- Experience with evaluation and observability for AI systems (eval harnesses, tracing, drift and quality monitoring)
- Strong stakeholder communication skills, with the ability to translate architectural concepts into actionable delivery guidance and to hold a position with technical leaders when the situation calls for it
- Comfort partnering directly with engineering teams on solutioning, including light-weight prototyping and technical deep-dives
Preferred Qualifications- Experience operating in regulated or high-governance environments
- Experience with data architecture for AI (vector stores, search indexes, document and knowledge pipelines)
- Familiarity with MLOps practices and production monitoring tooling for AI workloads
- Experience within staffing, workforce solutions, or HR technology
- Prior experience supporting multiple delivery teams within a Center of Excellence or enterprise architecture function
Additional InformationBenefits are subject to change and may be subject to specific elections, plan, or program terms. This role is eligible for the following:
- Medical, dental, & vision
- 401(k)/Roth
- Insurance (Basic/Supplemental Life & AD&D)
- Short and long term disability
- Health & Dependent Care Spending Accounts (HSA & DCFSA)
- Transportation benefits
- Employee Assistance Program
- Tuition assistance
- Time off/Leave (PTO, primary caregiver/parental leave)
*Location disclaimer: this position is open to North America locations outside of California, Colorado, New Jersey, New York and Washington.
