Solutions Engineer (Chicago)

LangChain langchain.com Open · verified Sep 13, 2026

Remote Cincinnati Full-time Mid-level Solutions & Sales Engineering

Mid-level Solutions & Sales Engineering role at LangChain. Remote, United States · full-time · $200,000–$250,000 base.

On-target earningsStated by LangChain

$200,000 – $250,000

On-target earnings for this role, in USD per year, as published in the posting. Base plus commission; the split is in the posting.

Role brief

Growth Roles summary, based on the employer's posting.

What you'll do

You'll scope evaluations with account executives, lead discovery, and build POCs around real customer use cases
You'll lead architecture reviews and answer security, infrastructure, and competitive evaluation questions
You'll build and deploy production AI agents with customer engineering teams, from prototypes through rollout
You'll deliver demos, workshops, trainings, and technical enablement for developer audiences
You'll advise customers after purchase, share field feedback, and create reusable POCs, cookbooks, and examples

What you bring

You bring at least six years in a relevant technical role, including solutions, sales, customer, or software engineering
You can own discovery, POCs, architecture reviews, and competitive evaluations across a sales cycle
You explain technical tradeoffs clearly, build developer trust, and turn that into customer decisions
You take responsibility for outcomes beyond recommendations and are interested in running AI agents in production
You move quickly and are willing to figure things out as you go

Who this fits

This suits a hands-on technical seller who works across engineering, product, and sales and wants customer-facing AI agent deployments rather than demos or research. The role is remote and listed in Cincinnati, while the title says Chicago. You'll work with account executives and customer engineering teams; the listing does not state people-management or travel requirements.

From the employer

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

Compensation

Annual OTE range: $200,000–$250,000 USD

Looking for a hands-on implementation role focused on custom delivery, bespoke integrations, and scoped consulting projects? Please check out the Deployed Engineer (Professional Services) roles.

Compensation Philosophy:

We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. Actual compensation and offerings will vary based on role, level, and location. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations.

Benefits

Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.