Solutions Engineer (Chicago)

You will work on some of the hardest problems in applied AI, in front of customers, on a clock. Not demos, not research: systems real teams depend on in production. The feedback loop is fast, the impact is measurable in closed deals and live deployments, and the work directly shapes how AI agents get built in the real world.

Own the technical win. Partner with AEs to scope evaluations, run technical discovery, and design POCs that map to the customer's real use case rather than a canned demo

Be the technical authority in the room during architecture reviews, security and infrastructure questions, and head-to-head evaluations

Co-architect and co-build production AI agents with customer engineering teams, from prototype through rollout

Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows

Run demos, trainings, and workshops for developer audiences, from single-team sessions to larger technical enablement

Advise customers post-sale on architecture, best practices, and roadmap-level decisions, and find the expansion opportunities that come out of those conversations

Surface field feedback to product and build reusable POC assets, cookbooks, and example code that scale across accounts

Contribute code upstream when it meaningfully improves customer outcomes

6+ years in a relevant technical role such as solutions engineering, sales engineering, customer engineering, software engineering, or founding and product engineering, ideally at a startup or scale-up

Comfort owning the technical thread in a sales cycle: discovery, POCs, architecture reviews, and competitive evaluations

Ability to explain technical tradeoffs clearly and build trust with developer audiences, then translate that into a decision the customer is ready to make

A track record of taking responsibility for outcomes, not just recommendations

A bias toward action and a willingness to figure things out as you go

Genuine interest in operating AI agents in production, not just building demos

You've deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks

Experience carrying a technical number or working against pipeline alongside a sales team

Experience with LLM evaluation, observability, or guardrails

Experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts