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ABOUT US
At Lang Chain, 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.
Today, Lang Chain, Lang Graph, Lang Smith, and Agent Builder are used by teams shipping real AI products across startups and large enterprises. Millions of developers trust Lang Chain to power AI teams at companies like Replit, Clay, Coinbase, Workday, Lyft, Cloudflare, Harvey, Rippling, Vanta, and 35% of the Fortune 500.
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. Lang Chain is a place where your contributions can shape how this technology shows up in the real world.
ABOUT THE TEAM:
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. The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the Lang Chain suite.
Deployed Engineers sit at the intersection of engineering, product, and go-to-market, shaping how Lang Chain is adopted in the field and feeding real-world insights back into the platform.
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.
WHAT YOU’LL DO:
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Co-architect and co-build production AI agents with customer engineering teams
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Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations
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Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows
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Advise customers post-sale on architecture, best practices, and roadmap-level decisions
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Run technical demos, trainings, and workshops for developer audiences
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Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers
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Occasionally contribute code upstream when it meaningfully improves customer outcomes
WHAT YOU’LL BRING:
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3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up
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Strong Python, JavaScript and systems fundamentals
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Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling
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Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations
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Can explain technical tradeoffs clearly and build trust with developer audiences
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Take responsibility for outcomes, not just recommendations
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Have a bias toward action and enjoy figuring things out as you go
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Are excited about operating AI agents in production, not just building demos
NICE TO HAVE’S:
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You’ve deployed AI agents in production, especially using Lang Chain, Lang Graph, or similar frameworks
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Worked with LLM evaluation, observability, or guardrails
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Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts
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Have shipped and operated production software and are comfortable owning systems under real-world constraints
COMPENSATION & BENEFITS:
We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks. Benefits include things like medical, dental, and vision coverage, flexible vacation, a 401(k) plan, and life insurance. 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.
Annual OTE range: $150,000–$250,000 USD
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LangChain 소개

LangChain
Series BA platform that provides open-source frameworks and tools for engineering and deploying language model agents.
51-200
직원 수
San Francisco
본사 위치
$200M
기업 가치
리뷰
3.4
3개 리뷰
워라밸
2.5
보상
3.0
문화
2.8
커리어
3.2
경영진
2.3
35%
친구에게 추천
장점
Working with cutting-edge AI technologies like LangChain and RAG
Hands-on experience building end-to-end AI projects
Exposure to modern applied AI development
단점
Uncertainty about long-term career prospects and employability
Projects rarely make it to production use
Lack of senior developer mentorship and guidance
연봉 정보
13개 데이터
Mid/L4
Senior/L5
Mid/L4 · Product Designer
1개 리포트
$178,619
총 연봉
기본급
$155,147
주식
-
보너스
-
$178,619
$178,619
면접 경험
10개 면접
난이도
2.7
/ 5
소요 기간
14-28주
합격률
60%
경험
긍정 50%
보통 40%
부정 10%
면접 과정
1
Application Review
2
Recruiter Screen
3
Technical Assessment/Take-home
4
Technical Interview
5
Virtual Onsite/Final Round
6
Offer
자주 나오는 질문
System Design
Machine Learning/AI Knowledge
Coding/Algorithm
Technical Architecture
Behavioral/STAR
뉴스 & 버즈
LangChain, Langflow, LiteLLM: When AI’s Foundation Code Becomes the Attack Surface - Security Boulevard
Security Boulevard
News
·
1w ago
DataCamp and LangChain Partner to Launch AI Engineering Learning Track - Business Wire
Business Wire
News
·
2w ago
Finally made it ugghhhh
https://preview.redd.it/61yq8yyinnrg1.png?width=462&format=png&auto=webp&s=1f821e417ef14ac8d60ecd0714297886a81586fa Hey I am 3rd year CS undergrad from Lower NIT, I started out my journey seriously in 2nd year by learning MERN nd stuff did an internship at the end of 4th sem (which was unpaind and work from home). During summer vacation I realised this web dev stuff is not for me and switched to learning ML nd boy I loved this domain (maybe since I sucked at web dev i was under pres
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3w ago
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19
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23
LangChain Releases Comprehensive Agent Evaluation Checklist for AI Developers - mexc.com
mexc.com
News
·
3w ago