Associate Principal, AI Solution Architecture

Vizient, Inc

. Establish architectural standards and design patterns for LLM-enabled workflows, agentic systems, and multi-agent orchestration .

Posted 9/17/2026full-timeEdina • Illinois • United StatesSeniorLead💰 $156,500 - $290,100 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive experience in enterprise software architecture and AI/ML systems, with a strong focus on LLM orchestration, data governance, and compliance in regulated environments. Capable of establishing architectural standards, leading technical evaluations, and collaborating across diverse teams to deliver robust platform solutions.

Highest-signal resume keywords

Enterprise Software ArchitectureLLM OrchestrationData GovernanceAI/ML Platform InfrastructureHealthcare Data Compliance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

LLM Evaluation PracticesContext ManagementModel Provenance TrackingDeployment PipelinesObservability StandardsArchitectural StandardsTechnical DocumentationSystems ThinkingRisk AssessmentVersion Governance

Soft Skills

Strong Communication SkillsCollaboration with StakeholdersDecision-Making

Tools & Technologies

Cloud AI Platform IntegrationMLOps ToolingTelemetry SystemsOperational MonitoringFinOps Reporting

Industry Keywords

HIPAAPHIClinical Data SharingCompliance ControlsAI Governance Frameworks

Tech Stack

Tools & technologies

Cloud

About the role

Key responsibilities & impact

Establish architectural standards and design patterns for LLM-enabled workflows, agentic systems, and multi-agent orchestration
Define hybrid system patterns balancing LLM-based judgment with deterministic code for high-confidence and compliance-sensitive operations
Architect the platform component layer, including shared libraries, reusable agent templates, RAG pipelines, vector databases, and hybrid search patterns
Define contribution models and reusable patterns for engineering squads
Define the AI Bill of Materials approach, model provenance tracking, third-party model risk assessment, and version governance
Establish observability standards for telemetry, reasoning traceability, operational monitoring, and auditability
Design routing architectures optimizing latency and cost, including semantic caching, model fallback, and workload distribution between SLMs and LLMs
Define technical exit criteria and architectural standards for sandbox-to-production graduation
Partner with AIOps and pilot teams to transition initiatives into governed platform environments
Architect data models for enterprise agent registries and context-management strategies, including memory architecture and resource connectivity
Lead technical evaluations of AI technologies, platforms, and protocols and document architectural rationale
Architect healthcare data privacy and security boundaries for PHI and PII
Ensure RAG, memory, and context-management systems comply with HIPAA and clinical data-sharing agreements
Define year-one milestones for Vizient’s AI maturity model
Establish evaluation and validation methodologies, including LLM-as-judge calibration, prompt testing, regression testing, and probabilistic-system evaluation
Integrate evaluation and validation into engineering and deployment pipelines
Partner with pilot teams to identify reusable architectural patterns and strengthen the platform component layer
Design rate-limiting, token-throttling, cross-charge, cost-allocation, and FinOps reporting architectures
Serve as technical design authority for delivery pods and own solution blueprints aligned with AI-DLC gates
Partner with Enterprise Architecture to integrate the AI stack into Vizient’s enterprise architecture blueprint
Collaborate with Engineering and Operations on deployment automation and policy-as-code governance
Partner with Security, Clinical, Data, and Product teams to translate requirements into platform capabilities
Present architecture positions, technical evaluations, and recommendations to leadership and cross-functional stakeholders
Maintain reference architectures, technical standards, and decision trees for distributed engineering teams

Requirements

What you’ll need

12 or more years of experience in enterprise software architecture, platform architecture, AI/ML systems, data architecture, or enterprise platform delivery, including experience operating at enterprise scale
At least 2 years of dedicated, hands-on experience delivering LLM orchestration and agentic systems in production, including multi-agent orchestration, context management, and tool connectivity
Strong systems-thinking skills, with the ability to evaluate emergent behavior, failure modes, and downstream architectural consequences before solutions are implemented
Demonstrated ability to make, communicate, and defend principled architectural decisions
Experience designing AI/ML platform infrastructure, including cloud AI platform integration, LLMOps/MLOps tooling, deployment pipelines, observability, and runtime monitoring
Experience with LLM evaluation practices, including LLM-as-judge calibration, regression pipelines, prompt testing, and validation of probabilistic systems
Strong written and verbal communication skills, with the ability to create architecture decision records, technical standards, reference architectures, and other technical documentation
Demonstrated ability to collaborate effectively with stakeholders across Engineering, Security, Compliance, Data, Product, and business functions
Experience working in regulated data environments, including data governance, audit readiness, privacy boundaries, and compliance controls
Preferred: Deep familiarity with healthcare data environments, including HIPAA, PHI handling, and clinical data-sharing constraints
Preferred: Experience building or governing enterprise AI/ML platforms, including agent registries, model-provenance tracking, AIBOM concepts, and AI governance frameworks
Preferred: Demonstrated experience building reusable platform components, such as shared libraries, templates, run patterns, reference architectures, or developer-enablement assets
Preferred: Experience creating platforms and tools that accelerate downstream engineering teams while minimizing unnecessary dependencies
Preferred: Demonstrated ability to earn adoption through product quality, technical credibility, and peer influence

Benefits

Comp & perks

Incentive eligible
Comprehensive benefits plan