This role is the main escalation point for technical support questions from AI engineers and infrastructure architects, diagnosing problems across customer setups, the company’s products, and deployments. A strong candidate brings several years of technical support, solutions engineering, or SRE work in demanding B2B settings, with expert troubleshooting across APIs, SDKs, distributed systems, and cloud stacks. Python programming, container tooling, and comfort reading logs, traces, and metrics round out the profile.
How it fits the AI era
The work sits where AI agents meet production, so support means debugging live LLM applications and helping teams improve observability and evaluations. AI can speed up triage and drafting, but judgment about root causes, clear writing, and calm handling of critical incidents stay human. The role also shapes how support itself is done for AI platforms, which is still being defined.
How it builds AI experience
A person here would gain hands on exposure to agent frameworks, observability and evaluation tooling, and the debugging patterns of production LLM systems. Building that means getting fluent with the company’s open source frameworks and platform, practicing trace and metric analysis, and learning how LLM providers and OpenTelemetry integrations behave under load.
Worth showing
- API and SDK troubleshooting
- Python programming
- Kubernetes and Docker
- Logs traces metrics analysis
This is our own read, written from the employer’s posting and the company’s Spotlight. The employer’s posting is the source of truth for the role.
About LangChain
LangChain provides an open agent engineering platform and open source frameworks for building, testing, deploying, monitoring, and governing AI agents, per the company.
#J-18808-Ljbffr…