Senior Solutions Architect / Principal AI Solutions Architect

Senior Solutions Architect / Principal AI Solutions Architect

Minneapolis, MN | Chicago, IL | Irving, TX – Onsite

Role Overview

We are seeking a Senior Solutions Architect / Principal AI Solutions Architect with strong experience in enterprise architecture, AI/ML platforms, and production-grade LLM/agentic systems. The ideal candidate will drive scalable AI architecture while addressing reliability, governance, security, and regulated-data requirements.

Key Responsibilities

• Architect and deliver LLM orchestration and agentic AI systems, including multi-agent workflows, context management, and tool connectivity.
• Design AI/ML platform infrastructure, including cloud AI integration, LLMOps/MLOps, deployment pipelines, observability, and runtime monitoring.
• Establish LLM evaluation frameworks, including LLM-as-judge calibration and regression testing for probabilistic systems.
• Evaluate architectural tradeoffs, failure modes, system behavior, and downstream impacts.
• Develop architecture decision records, technical standards, and reusable platform patterns.
• Partner with engineering, security, compliance, and business teams to drive enterprise AI adoption.
• Design architectures supporting data governance, privacy, audit readiness, and regulated environments.

Required Qualifications

• 12+ years in enterprise software/platform architecture, AI/ML systems, data architecture, or enterprise platform delivery.
• 2+ years of hands-on production experience with LLM orchestration and agentic AI systems.
• Strong systems-thinking and enterprise architecture capabilities.
• Experience with AI/ML platforms, LLMOps/MLOps, cloud AI services, CI/CD, observability, and monitoring.
• Experience with LLM evaluation, LLM-as-judge, calibration, and regression pipelines.
• Strong written and verbal communication skills with experience creating architecture and technical documentation.
• Experience with regulated data environments, governance, privacy, and audit requirements.

Preferred Experience

• Healthcare/clinical data environments, including HIPAA and PHI.
• Enterprise AI governance, agent registries, model provenance, AIBOM, or AI governance frameworks.
• Building reusable platform components, shared libraries, templates, and developer enablement tools.
• Experience driving adoption through technical leadership, product quality, and cross-functional influence.