AI Domain Architect

Allegis Global Solutions•Hanover, MD

About The Position

The 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.

Requirements

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

Nice To Haves

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

Responsibilities

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
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
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
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
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