Agent Reliability Engineer, GTM

📋 Job Overview

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.

🏢 About LangChain

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 Role

You'll own the health, cost, performance, and business impact of the GTM Agent, and build the feedback loops that keep it improving. Because we build the platform we run on, you'll also operate the agent on LangSmith the way we tell customers to, and turn that practice into the reference story enterprises keep asking us for. You'll work across Python 3.11, FastAPI, LangGraph, DeepAgents, LangSmith, Supabase Postgres, BigQuery, Anthropic and OpenAI models, and Slack and Next.js surfaces.

✅ Key Responsibilities

• Monitor production health across every graph, catching errors, slow runs, expensive runs, and silent failures before reps report them
• Triage incoming issues from Slack, tickets, and rep reports, fixing small things directly and routing the rest to the right owner
• Run the weekly eval suite, investigate failures, and turn real production bugs into permanent regression tests
• Track cost and latency by model, graph, use case, and role, and recommend concrete changes to model choice, reasoning effort, and caching
• Track usage and adoption per rep and per feature, and own the weekly health report the team runs on
• Build the business metrics that show leadership what the agent is worth, from reply rates and meetings booked to hours reclaimed and ROI
• Build our own monitoring and alerting on LangSmith, and write the “how we run our own agent” story for customers

📌 Required Qualifications

• Strong production Python and SQL, comfortable working in traces, logs, and warehouse tables
• Real experience running LLM applications, including tracing, evals, and prompt and cache mechanics
• SRE or production operations instincts: percentiles, SLOs, and separating noise from real pattern
• Healthy skepticism about metrics; you check what a number actually counts before you publish it
• Clear writing, and interest in publishing what you learn
• High agency; you notice what's missing and take initiative to build it

⭐ Desirable Experience

• LangGraph or LangSmith experience
• Experience building an eval suite from scratch
• BigQuery or dbt
• Prior DevRel-adjacent writing
• Empathy for sales and go-to-market users

🎁 Benefits

Salary: $150,000 - $190,000

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

🛂 Visa & Eligibility

No visa or eligibility information provided.