Level

LangChain

Deployed Engineer, Professional Services

LangChain is hiring a Deployed Engineer, Professional Services for a remote role open to applicants in United States. It pays $150k-$270k a year and Level rates it ; you can apply on LangChain.

Level's read on this job

  • On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
  • The posting lists this role as remote.
  • Tools and skills the posting cites include LangGraph, Hugging Face and Clay.
  • Level classifies it as a senior role.
  • Level lists 103 other open roles at LangChain.

AI in this role

Design and build production-grade AI agents and evaluation pipelines directly for enterprise customers as a Deployed Engineer.

langgraphhugging-faceclaypython
ai-agentsfine-tuningagent-architecturellm-evaluationorchestrationconversational-ui
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.

  • Willing to travel up to 20% of the time.

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

Remote

Compensation

$150,000-$270,000 base + equity

Looking to lead technical strategy across the customer journey from Proofs of Value (PoVs) to driving overall adoption? Please check out the Deployed Engineer listing focused on Account Strategy & Solutions.

Compensation Philosophy:

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. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. 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.

How we rate this

Deployed Engineer, Professional Services at LangChain rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

AI agentsFine-tuningAgent ArchitectureLLM EvaluationOrchestrationConversational UILangGraphHugging Face

Questions you could be asked

  1. How do you decide when an AI agent can act on its own versus asking for approval first?
  2. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  3. Tell me about a project where agent architecture was part of your work. What did you do?
  4. Tell me about a project where llm evaluation was part of your work. What did you do?
  5. Tell me about a project where orchestration was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: AI agents, Fine-tuning, Agent Architecture, LLM Evaluation, and Orchestration. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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