Staff AI Engineer

About the role

We are looking for a Staff AI Engineer to join an internal AI Engineering team supporting a portfolio of internal and customer-facing enterprise initiatives. This is a hands-on technical leadership role, roughly split between building and technical mentorship. You'll architect and develop production AI systems while helping establish technical direction, make key architectural decisions, and elevate the engineers around you. The ideal candidate combines strong software engineering fundamentals, hands-on AI experience, and product/customer sense. You should be comfortable taking an ambiguous problem from initial discovery through architecture, development, deployment, and iteration. The team evaluates AI Engineers across five core areas: full-stack engineering, AI systems design, production quality and evaluation, customer/product judgment, and architecture/organizational influence with particular emphasis on architecture and ownership for this Staff-level position.

Interview Process: Approximately 3 rounds, beginning with two back-to-back 30-minute technical conversations, followed by a potential final round.

Responsibilities

• Architect, build, and deploy AI systems from the ground up into production
• Remain hands-on while providing technical direction and mentorship to other AI Engineers
• Design solutions using LLMs, agentic workflows, RAG/embeddings, model APIs, and traditional software components
• Make and communicate architectural decisions around technologies, models, data, scalability, reliability, security, and cost
• Partner with customers, product teams, and business stakeholders to understand problems and shape solutions
• Establish strong practices around AI evaluation, observability, monitoring, and production reliability
• Create reusable patterns and engineering standards that can be leveraged across multiple AI initiatives
• At the Staff level, the expectation is someone who can lead architecture across a product or domain, create reusable technical patterns, and raise the technical judgment of the broader team.

Requirements

• Production AI: Personally built and deployed AI systems from scratch into production; experience limited to AI integrations or POCs will not be enough
• Architecture Ownership: Experience selecting technologies and patterns, designing systems, and clearly communicating technical tradeoffs
• Hands-on Engineering: Strong software engineering foundation and still actively coding/building
• Applied AI: Strong experience with LLMs, agents, RAG/embeddings, model selection, and modern AI application architecture
• Product Sense: Experience working directly with customers/users and translating ambiguous business problems into technical solutions
• Production Maturity: Experience with AI evals, observability, reliability, latency, cost, failure handling, and improving systems after deployment
• Technical Leadership: Experience mentoring engineers and influencing technical direction without moving away from hands-on development

Qualifications

• Experience building 0‑1 AI products
• Startup, entrepreneurial, or high‑ownership engineering background
• Forward‑deployed or other customer‑facing engineering experience
• Multimodal AI experience, including voice or image
• Experience developing reusable AI platforms, frameworks, or engineering standards
• Advanced agentic AI experience
• Experience influencing architecture across multiple products or teams

Skills

• Python, SQL, PostgreSQL, React, C#, iOS, LLMs, agentic AI, RAG/embeddings, and cloud‑based development. Candidates do not need every technology listed. Strong engineering fundamentals, AI depth, and the ability to work across the stack are more important.

Schedule

Hybrid preferred ideally 2‑3 days/week in Milwaukee. Chicagoland candidates who can average around 2 days/week in Milwaukee are also ideal. Not a hard requirement for the right candidate.