Deployed Engineer (NYC)
LangChain · New York, NY
- On site
- Full-time
- $272,500 / year
- New York, NY
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Subject: Interested in the Deployed Engineer (NYC) role at LangChain
Hi Morgan — I came across the Deployed Engineer (NYC) opening and wanted to reach out directly. I've spent the last few years doing exactly this kind of work, and LangChain stood out because…
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Job highlights
- Build and deploy AI agents in production.
- Partner with customers on technical solutions.
- Focus on applied AI systems, not demos.
- Requires Python, JavaScript, and systems skills.
- Shape the future of AI agent adoption.
About the role
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 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 LangChain suite.
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.
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
- 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
- Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows
- Advise customers post-sale on architecture, best practices, and roadmap-level decisions
- Run technical demos, trainings, and workshops for developer audiences
- Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers
What You’ll Bring
- 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up
- Strong Python, JavaScript and systems fundamentals
- Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling
- Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations
- Can explain technical tradeoffs clearly and build trust with developer audiences
- Take responsibility for outcomes, not just recommendations
- Have a bias toward action and enjoy figuring things out as you go
- Are excited about operating AI agents in production, not just building demos
Nice to Have’s:
- You’ve deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks
- Worked with LLM evaluation, observability, or guardrails
- Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts
- Have shipped and operated production software and are comfortable owning systems under real-world constraints
Compensation
Annual OTE range: $165,000–$380,000 USD
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.
Key skills/competency
- Deployed Engineer
- AI Agents
- Production Systems
- Customer Engineering
- LLM Applications
- Python
- JavaScript
- Cloud Environments
- System Design
- Technical Evaluation
Skills & topics
- Deployed Engineer
- AI Agents
- LLM
- Python
- JavaScript
- Software Engineering
- Customer Engineering
- Solutions Engineering
- Startup
- Production Systems
How to get hired
- Tailor your resume: Highlight Python, JavaScript, and AI agent design experience.
- Showcase customer-facing skills: Emphasize POCs, architecture reviews, and trust-building.
- Demonstrate initiative: Detail your bias for action and problem-solving.
- Prepare technical examples: Be ready to discuss deployed AI agents and LLM applications.
- Understand LangChain: Familiarize yourself with their products and mission.
Technical preparation
Behavioral questions
Frequently asked questions
- What is the typical career path for a Deployed Engineer at LangChain?
- The Deployed Engineer role at LangChain offers a unique opportunity to influence the direction of AI agent development. Your career path could lead to senior technical advisory roles, specialized AI architecture positions, or even management within the customer-facing engineering teams. Your direct impact on production systems and customer success provides a strong foundation for growth within the company.
- How does LangChain support professional development for its Deployed Engineers?
- LangChain fosters professional development through hands-on experience with cutting-edge AI technologies and direct customer interaction. The fast feedback loop from production systems and the opportunity to surface field insights for product improvement offer continuous learning. You'll also engage in technical demos, trainings, and workshops, constantly expanding your expertise in applied AI.
- What kind of AI agents will I be working with as a Deployed Engineer at LangChain?
- As a Deployed Engineer at LangChain, you'll work on a variety of AI agent applications crucial for real-world business operations. This includes conversational agents, sophisticated research agents, and complex multi-step workflows. The focus is on building robust systems that teams rely on, moving beyond simple demos to impactful production deployments.
- What is the role of a Deployed Engineer in customer pre-sales at LangChain?
- In pre-sales, the Deployed Engineer is pivotal in achieving the technical win. This involves co-architecting and co-building proof-of-concepts (POCs), providing in-depth technical answers, and guiding customer evaluations. Your ability to clearly explain technical trade-offs and build trust with developer audiences is key to demonstrating LangChain's value.
- How does LangChain ensure a fast feedback loop for its Deployed Engineers?
- LangChain's model, starting from open-source tools and growing into a platform with significant production adoption, naturally creates a fast feedback loop. Working directly with customers on production systems allows for immediate insights into what works and what can be improved. This real-world data directly informs product development and the creation of reusable patterns and cookbooks.
- What differentiates LangChain's approach to AI agents from other companies?
- LangChain differentiates itself by focusing on making intelligent agents ubiquitous through robust engineering foundations for real-world applications. Unlike research-focused or demo-centric approaches, LangChain emphasizes building production-ready AI agents that teams can rely on. Their comprehensive platform, including LangSmith for observability and deployment, supports agents at scale, from open-source frameworks to enterprise solutions.
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