AI Engineer

Job Title:   Staff AI Engineer
Location:   Milwaukee, WI / Raleigh, NC / Chicago, IL (Hybrid, 3 days Onsite a week)
Duration:   12+ Months

Face to Face Interview....

Job Description:
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.
What You'll Do
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.
Must Haves
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
Nice to Haves
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
Technical Environment
Python, SQL, PostgreSQL, React, C#, iOS, LLMs, agentic AI, RAG/embeddings, and cloud-based development.