Deployed Engineer, Pre-Sales (Dallas)

Dallas, TX · USD 200,000-300,000 / year · 6+ years experience · Full-time · Remote · Posted Oct 7, 2026

At a glance

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.

The Deployed Engineer works on challenging problems in applied AI, helping build systems that real teams depend on in production.
The role sits at the intersection of engineering, product, and go-to-market, shaping how LangChain is adopted in the field.

Co-architect and co-build production AI agents with customer engineering teams
Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations
Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows
Advise customers post-sale on architecture, best practices, and roadmap-level decisions
Run technical demos, trainings, and workshops for developer audiences
Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers
Occasionally contribute code upstream when it meaningfully improves customer outcomes
Travel to customers up to 25% of the time

6+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up
Strong Python, JavaScript and systems fundamentals
Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling
Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations
Can explain technical tradeoffs clearly and build trust with developer audiences
Take responsibility for outcomes, not just recommendations
Have a bias toward action and enjoy figuring things out as you go
Are excited about operating AI agents in production, not just building demos

You’ve deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks
Worked with LLM evaluation, observability, or guardrails
Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts
Have shipped and operated production software and are comfortable owning systems under real-world constraints