Solutions Engineer (Chicago)

About Us

At LangChain, our mission is to make intelligent agents ubiquitous. We build the foundation for agent engineering in the real world, helping developers move from prototypes to production-ready AI agents that teams can rely on. We began as widely adopted open-source tools and have grown to also offer a platform for building, evaluating, deploying, and operating agents at scale.

With $125M raised at Series B from IVP, Sequoia, Benchmark, CapitalG, and Sapphire Ventures, we’re at a stage where we’re continuing to develop new products, growth is accelerating, and all team members have meaningful impact on what we build and how we work together. LangChain is a place where your contributions can shape how this technology shows up in the real world.

Today, our platform includes LangSmith (Observability, Evaluation, Deployment, Fleet, and Sandboxes), our open source frameworks (LangChain, LangGraph, and Deep Agents), and the newly launched LangSmith Engine for autonomous agent improvement.

We have 100M+ monthly open source downloads, 6,000+ active LangSmith customers, and 5 of the Fortune 10 use LangSmith in production (+ 35% of the Fortune 500 overall), including teams at Klarna, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, LinkedIn, Monday.com, Nvidia, and Bridgewater.

About the team

The Deployed Engineering team is the technical front line of our go-to-market motion. We partner with account executives from the first technical conversation through production rollout, helping companies evaluate LangChain, prove it out on their hardest use case, and get agents running reliably at scale.

This is a hands-on, highly technical team. Solutions Engineers own the technical win: scoping evaluations, designing POCs that mirror real workloads, answering the deep architecture questions that decide a deal, and staying with the customer after signature to make sure what we sold actually ships.

We sit at the intersection of engineering, product, and sales. What we learn in the field shapes both how customers adopt LangChain and what we build next.

About the role

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.

What you'll do

• 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

What you'll bring

• 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
• Nice to haves
• 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