Principal AI Architect (Chicago)
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 LangChain, LangGraph, 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 investment. Mentor senior engineers and architects; raise the AI engineering bar across the organization. Track the evolving AI landscape and steer the enterprise toward durable, forward-looking choices. What You Bring (Required) 10 years in software engineering / enterprise architecture, with 5 years architecting and deploying production AI/ML systems at enterprise scale. (Exceptional candidates with less tenure but clearly stronger depth will be considered.) Demonstrated depth across the AI stack: Machine Learning & Deep Learning — model development, training, evaluation, and productionization. Computer Vision — detection, classification, segmentation, and deployment (including edge/real-time where relevant). LLMs / Generative AI — architecting and operationalizing LLM-driven applications; RAG, agents, fine-tuning, prompt/context engineering, function calling, and evaluation. AI Ops (MLOps/LLMOps) — CI/CD, observability, drift/retraining, reliability, and cost management in production. Proven track record designing enterprise-grade AI solutions on at least one major cloud (AWS, Azure, or GCP), including enterprise integration and API design. Deep, demonstrable experience delivering AI systems that are secure, robust, and well-governed — not just prototypes. Strong grasp of Responsible AI, model risk, and regulatory/compliance considerations. Fluency in Python and the modern AI/ML ecosystem (e.g., PyTorch/TensorFlow, Hugging Face, LangChain/LangGraph/Semantic Kernel, vector databases, containers/Kubernetes). Excellent communication — able to operate from executive strategy down to hands-on implementation. Bachelor's in Computer Science, Engineering, Data Science, or related field. Preferred / Top-Candidate Signals Master's or PhD in a relevant discipline, or equivalent demonstrated depth. Experience with agentic/multi-agent architectures in production. Background in a regulated or safety-critical / industrial enterprise environment. Contributions to open source, patents, publications, or recognized thought leadership in AI. Experience defining enterprise AI reference architectures adopted across multiple teams. Why This Role End-to-end ownership of the enterprise AI platform — you set the standard, not just implement it. Full-spectrum scope — classical ML, DL, CV, and modern LLM/agentic systems in one mandate. Real production impact at enterprise scale, with security and governance treated as first-class. Uncapped for the right person — compensation is not a barrier for a truly competitive candidate. J-18808-Ljbffr
