Deployed Engineer

LangChain

Deployed Engineer partnering with LangChain customers to build, deploy, and operate production AI agents. Designing architectures, leading technical evaluations, and feeding field insights into LangChain’s platform.

Posted 8/6/2026full-timeRemote • Illinois • 🇺🇸 United StatesMid-LevelSenior💰 $150,000 - $250,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and deploying AI agents, with a strong focus on customer engagement and technical leadership. Proficient in Python and JavaScript, with experience in architecting complex agent-based applications and guiding customers through technical evaluations and best practices.

Highest-signal resume keywords

Python ProgrammingJavaScript ProgrammingAgent-Based Application DesignCustomer EngagementTechnical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringSolutions EngineeringSystems FundamentalsMulti-Step WorkflowsOrchestrationFailure HandlingProduction AI Agent DeploymentLLM EvaluationObservabilityProduction Software Shipping

Soft Skills

Clear CommunicationTrust BuildingBias Toward ActionProblem Solving

Tools & Technologies

LangChainLangGraphAWSGCPAzureContainersKubernetes

Industry Keywords

AI AgentsConversational AgentsTechnical DemosWorkshopsCustomer Engineering

Tech Stack

Tools & technologies

AWSAzureGoogle Cloud PlatformJavaScriptKubernetesPython

About the role

Key responsibilities & impact

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
Occasionally contribute code upstream when it meaningfully improves customer outcomes
Travel to customers up to 40% of the time

Requirements

What you’ll need

6+ years in a relevant technical role, such as software engineering, customer engineering, solutions engineering, founding/product engineering
Experience ideally in a startup or scale-up
Strong Python, JavaScript, and systems fundamentals
Experience designing agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling
Comfortable working directly with customers during POCs, architecture reviews, and technical evaluations
Ability to explain technical tradeoffs clearly and build trust with developer audiences
Responsibility for outcomes, not just recommendations
Bias toward action and ability to figure things out as you go
Excitement about operating AI agents in production
Nice to have: production AI agent deployment, especially using LangChain, LangGraph, or similar frameworks
Nice to have: experience with LLM evaluation, observability, or guardrails
Nice to have: experience with AWS, GCP, Azure, containers, and basic Kubernetes concepts
Nice to have: experience shipping and operating production software and owning systems under real-world constraints

Benefits

Comp & perks

Medical coverage
Dental coverage
Vision coverage
Flexible vacation
401(k) plan
Meals on in-office days in the US
Variable compensation for relevant roles
Meaningful equity
Competitive benefits and perks