Deployed Engineer, Professional Services (San Francisco)

Langchain
San Francisco, CA
Hybrid

Who this role is best for

Deployed Engineers with hands-on experience in production AI systems and strong client-facing communication skills will find this role's embedded delivery model and technical depth appealing.

Best fit for

  • Candidates with experience building production agents and guiding enterprise teams through technical decisions
    — “You'll translate vague enterprise workflows into concrete software specs and guide engineering teams through the resulting solution
  • Individuals who can operate across advisory and embedded roles, adapting to varying levels of client engagement
    — “Comfortable operating across the full spectrum from advisory to embedded delivery
  • Professionals with deep Python knowledge and familiarity with multi-agent systems and state management
    — “Strong experience with LangChain/LangGraph/Deep Agents or comparable frameworks, including multi-agent patterns and state management

Things to consider

  • This role demands a high level of client interaction and long-term project engagement
    — “Serve as a deployed engineer inside the customer's team for extended engagements
  • The position requires expertise in evaluating non-deterministic AI systems, which may be uncommon in other roles
    — “Deep familiarity designing and implementing evaluation methodologies for non-deterministic AI systems

How to stand out

  • Emphasize your experience in translating enterprise workflows into technical specifications
    — “You'll translate vague enterprise workflows into concrete software specs
  • Highlight your ability to lead and collaborate with engineering teams on complex agent systems
    — “guide engineering teams through the resulting solution, or build it for them
  • Showcase your hands-on delivery experience with production agent systems, not just theoretical knowledge
    — “You are someone who's built real AI systems for production and can defend the technical tradeoffs within them
  • Demonstrate your ability to work with Python and AI frameworks such as LangChain or LangGraph
    — “Strong experience with LangChain/LangGraph/Deep Agents or comparable frameworks
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • build reliable, production agents
  • co-build evaluation pipelines
  • ship agent systems directly
Typical background
4+ years software engineering2+ years production agent systems

Skills & requirements

Required

Software EngineeringPythonAgent Architecture DesignEvaluation MethodologiesLlm-heavy Applications

Preferred

TypeScriptLangchainLanggraphDeep AgentsPost-training Techniques

Stack & domain

PythonTypeScriptJavaScriptCommunicationAIProduction Systems

About the role

Original posting from Langchain via Ashby

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 ROLE

We're looking for a Deployed Engineer to join our Professional Services team, working directly with enterprise customers to build reliable, production agents. You'll translate vague enterprise workflows into concrete software specs and guide engineering teams through the resulting solution, or build it for them. You might spend a week designing a customer's agent architecture, a few weeks co-building their evaluation pipeline, or a quarter embedded inside their team shipping alongside their engineers. You are someone who's built real AI systems for production and can defend the technical tradeoffs within them.

KEY RESPONSIBILITIES

  • Advising: Agent architecture design, evaluation strategy review, and best-practice production guidance.
  • Building: Co-build with the customer's engineering team across the full Agent Development Lifecycle (ADLC) in outcome-scoped engagements.
  • Embedding: Serve as a deployed engineer inside the customer's team for extended engagements, operating as a de facto member of their org to ship agent systems directly.
  • Agent Engineering: ADLC end-to-end, architecture design, orchestration patterns, evals, custom conversational UIs, and production deployment.
  • Applied AI: Post-training, supervised fine-tuning, harness engineering, trace mining, model selection and evaluation methodology.

REQUIREMENTS

  • 4+ years of software engineering experience with deep expertise in Python. TypeScript/JavaScript a plus.
  • 2+ years of hands-on experience building and shipping production agent systems.
  • Strong client-facing communication skills, with the ability to confidently articulate architectural decisions to technical stakeholders (engineers, architects, CTOs).
  • Strong experience with LangChain/LangGraph/Deep Agents or comparable frameworks, including multi-agent patterns and state management (short and long-term memory).
  • Deep familiarity designing and implementing evaluation methodologies for non-deterministic AI systems.
  • Comfortable operating across the full spectrum from advisory to embedded delivery.

NICE TO HAVE

  • Exposure to dataset curation and post-training techniques (SFT, DPO, RLHF) on open-weight models using tools like Axolotl, Unsloth, Hugging Face transformers, or TRL.
  • Experience with trace mining to drive continuous improvement loops

LOCATION

San Francisco, CA

COMPENSATION

$150,000-$215,000 base + equity

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.

Source: Langchain careers (Ashby)

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