Applied AI Engineer, Marketing
About LangChain
At LangChain, our mission is to make intelligent agents ubiquitous. We help developers build mission-critical AI applications across the entire agent development lifecycle. Our open source frameworks LangChain and LangGraph see over 70+ million downloads per month. Developers rely on LangChain for composable integrations and LangGraph for controllable agent orchestration. Our commercial agent platform, consisting of LangSmith and LangGraph Platform, enables teams to build, test, run, and manage agents at scale across their organization.
Founded in 2023, LangChain powers top engineering teams at companies like Replit, Lovable, Clay, Klarna, LinkedIn, and more.
About the role:
We are looking for an Applied AI Engineer to join our Marketing Operations team. This is a hands-on role focused on building LangChain and LangGraph-powered agents that make our go-to-market teams more efficient. You’ll partner with Marketing Ops, Sales Ops, Growth and Data teams to design, prototype, and deploy agents that automate workflows, run analysis, and accelerate pipeline generation.
This role is ideal for someone excited to apply cutting-edge AI workflows to real-world business operations. It’s not a research role, we’re looking for a builder who can move quickly from prototype to production.
What you will do:
Lead transformative AI initiatives using LangChain and LangGraph that improve GTM efficiency, unlock new ways for marketers and sellers to work with data, and drive an AI-first mindset across the go-to-market organization.
Partner with Marketing, Sales, Data, and Engineering to identify high-impact opportunities for internal AI adoption.
Design, prototype, and deploy agentic applications that automate workflows and deliver measurable business value.
Own projects end-to-end, from experimentation to production deployment, ensuring reliability and scalability.
Translate emerging AI trends into actionable strategy, technical guidance, and internal enablement.
Communicate the impact and ROI of AI-powered initiatives to stakeholders across the company.
Act as a force multiplier by building production-ready solutions and mentoring others to explore and implement AI within LangChain.
How to be successful in this role:
Product-minded. Ability to translate business problems into working solutions. Shipping an MVP and iteratively improving and adding scope.
Experience: 1+ years building AI applications professionally, and 3+ years in software engineering (startup experience a plus).
Applied AI skills: Track record of implementing and evaluating LLM-driven systems (workflows, RAG pipelines, and autonomous agents).
Technical depth: Expertise in the core components of AI applications, including prompting and context engineering, retrieval methods, agent architectures, vector databases, and LLM inference APIs (with knowledge of which models perform best for specific tasks, e.g., Gemini for multimodal).
Engineering skills: Expert in Python or TypeScript (ideally both), with the ability to build full-stack applications end-to-end.
Communication: Strong written and verbal skills; able to clearly explain complex technical concepts to technical and non-technical stakeholders.
Startup DNA: Comfortable in fast-moving, unstructured environments. You see ambiguity as an opportunity to define impactful work and shape the company’s direction.
Curiosity: Naturally driven to stay current on AI research, tools, and best practices, and to keep growing as an engineer. Interest in B2B SaaS go-to-market workflows (sales/marketing/revops).
Nice to Have
Deep expertise with LangChain and LangGraph .
Professional proficiency in both Python and TypeScript .
Prior experience in applied AI research roles or AI-first startups.
Experience maintaining or contributing significantly to open-source projects.
Background in building and scaling full-stack applications (frontend + backend).
Compensation & Benefits
Competitive salary and equity commensurate with stage of company, role, and level of experience
Salary Range: $165,000 - $195,000
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