Principal AI Architect

We are looking for a Principal AI Architect to own the end-to-end architecture of our enterprise AI platform and to set the technical standard for how AI is designed, built, deployed, and governed across the company. This is a deeply technical, hands-on leadership role for someone who understands AI application architecture at a fundamental level and has repeatedly taken enterprise AI systems from concept to secure, robust, well-governed production at scale.

You will be the trusted technical authority partnering with engineering, data, security, risk, and business leaders to ensure every AI solution is architecturally sound, scalable, compliant with Responsible AI principles, and aligned with where the industry is heading. You will operate fluently across the full AI spectrum — classical machine learning, deep learning, computer vision, and modern LLM / agentic systems — and translate that breadth into reference architectures, guardrails, and production systems that teams across the enterprise build on.

The distance between identifying a customer problem and shipping a production solution is measured in weeks, and you ll own that entire journey, from the plant floor to the deployed system.

This role is based in Chicago and is on-site 5 days work from office.

Travel: Regular travel to customer sites, accross the US. Expect 50–60%. On non-travel weeks, you re in the Chicago office five days.

You ll spend time in factories, warehouses, engineering offices, planning meetings, and supply chain reviews, understanding how work actually gets done before deciding how software should improve it.

What You'll Do

Architecture & Technical Leadership

• Own the end-to-end architecture of the enterprise AI platform: orchestration layers, LLM integration patterns, RAG and vector pipelines, agent frameworks, feature stores, model-serving infrastructure, API mesh, and reusable shared services.
• Define architectural standards, reference designs, patterns, and guardrails that govern how engineering teams build and integrate AI workloads.
• Translate business and product requirements into concrete architecture decisions — selecting design patterns, evaluating and benchmarking frameworks, and assembling reusable components.
• Serve as the design authority in architecture reviews, ensuring solutions are scalable, secure, reliable, observable, and cost-efficient.

Design, Build & Deploy (Hands-On)

• Design and build production AI systems spanning classical ML, deep learning, computer vision, and generative/LLM workloads.
• Architect agentic AI systems — multi-agent orchestration, tool/function calling, memory systems, and planning/reasoning patterns — using frameworks such as Lang Chain, Lang Graph, and Semantic Kernel.
• Work hands-on with foundation models: prompt and context engineering, retrieval-augmented generation (RAG), fine-tuning/adaptation, evaluation, and custom model integration.
• Evaluate and integrate core AI infrastructure: vector databases, embedding models, orchestration layers, and observability/evaluation tooling.

Production, MLOps & LLMOps

• Establish and mature the full model lifecycle: CI/CD for models and prompts, automated testing, environment strategy, deployment, rollback, monitoring, drift detection, and retraining.
• Build observability, evaluation, reliability, and cost-management practices for LLM- and ML-based systems running in production.
• Ensure systems meet enterprise SLAs for latency, availability, and cost at scale.

Security, Robustness & Governance

• Embed security by design: data protection, secrets management, access control, tenant isolation, and defense against prompt injection, data exfiltration, model, and supply-chain risks.
• Operationalize Responsible AI and governance — safety, risk management, transparency, explainability, human-in-the-loop design, bias monitoring, and regulatory alignment (e.g., GDPR/CCPA, HIPAA, EU AI Act, and applicable industry regulations).
• Partner with security, legal, privacy, and risk teams to ensure compliance with enterprise architecture and data-governance standards.

Influence & Enablement

• Advise CIO/CTO and senior business leaders on AI strategy, build-vs-buy decisions, and platform…