LangChain

LangChain

Deployed Engineer, Pre-Sales (Austin)

Austin, TX · Senior

Sponsorship not specified$200k-$300kDetected 74 days ago
JavaScriptPythonAWSGCPAzureCloud PlatformsKubernetesLLMsAgentic AILangGraphHRISResearchLeadershipCommunication

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odds of building a lasting career here

61Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.

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Files H-1B transfers

9 transfer filings in the last year, covering 9 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

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About the role

  • The Deployed Engineer…You'll work on some of the hardest problems in applied AI - not demos, not research, but systems that real teams depend on in production.
  • The feedback loop is fast, the impact is visible, and the work you do directly shapes how AI agents are built in the real world.
  • The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the LangChain suite.

Responsibilities

  • 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
  • Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling
  • Can explain technical tradeoffs clearly and build trust with developer audiences
  • Are excited about operating AI agents in production, not just building demos

Requirements

  • Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts

Compensation

  • Final compensation will depend on experience, skills, and location.
  • We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks.
  • We are hiring for this role across a range of scope, and the compensation band reflects that.
  • Annual OTE range:
  • $200,000-$300,000 USD.

Benefits

  • Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.
  • Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations.

Company info

  • 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.
  • 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 Engineering team works directly with companies building and running AI agents in production, helping turn ideas and prototypes into systems teams can rely on.
  • This is a hands-on, highly technical team that partners closely with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work.
  • Deployed Engineers sit at the intersection of engineering, product, and go-to-market, shaping how LangChain is adopted in the field and feeding real-world insights back into the platform.
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This listing is sourced directly from LangChain's careers page and normalized into a canonical job model.