Senior Backend Software Engineer, AI Observability & Evals Platform (NYC)

present of AI; New York (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

LangChain seeks a Senior Backend Software Engineer for its AI Observability & Evals Platform in NYC. You’ll build and operate backend services and APIs powering tracing, monitoring, and evaluating AI apps at scale.

The role rewards strong API design, reliability practices, and cross‑team collaboration to keep production signals accurate and available. You’ll gain hands‑on experience with observability data models, agent signals, and evaluation workflows while working with product and frontend

Qualifications

• Several years of backend work on complex, high‑volume systems.
• Solid API and data service design.
• Hands-on tuning of databases and cloud infrastructure.
• Testing, monitoring, and root‑cause fixes are emphasized.
• Clear cross‑team communication and collaboration.

Responsibilities

• Build and run backend services and APIs behind an observability & evals platform.
• Prioritize reliability with testing, monitoring, and RCA driven fixes.
• Partner with product and full‑stack teams to scale the platform.

Skills

Backend development

API design

Distributed systems

Cross-team collaboration

Tools

Python

Go

Cloud infrastructure

Database tuning

Job description

Senior Backend Software Engineer, AI Observability & Evals Platform (NYC)

The role centers on building and running the backend services and APIs behind a platform for tracing, monitoring, and evaluating AI applications. A strong candidate brings several years of backend work on complex, high volume systems, solid API and data service design, and hands on tuning of databases and cloud infrastructure. Reliability habits such as testing, monitoring, and root cause fixes matter, along with clear cross team communication.

How it fits the AI era

The work sits close to how AI applications are watched and judged in production, so the systems must handle large streams of trace and evaluation data and stay dependable. AI tools can speed up routine coding and debugging, but architectural judgment, performance tradeoffs, and careful incident analysis remain human work. The role helps define what good observability looks like as agents move from prototypes into real use.

How it builds AI experience

A person here would gain direct exposure to evaluating and monitoring AI agents at scale, learning how traces, annotations, and evals are stored and queried. Building that background means getting comfortable with observability data models and evaluation workflows, and understanding how agent frameworks produce the signals teams rely on. Working alongside product and full stack colleagues on a platform used widely in production adds practical depth.

Worth showing

• Backend services at scale
• API and data design
• Database performance tuning
• Production debugging and reliability
• Python or Go

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.