Site Reliability Engineer job in San Francisco

As the SRE, you'll own the reliability and performance of the LiteLLM proxy in production. Our users run LiteLLM as a critical gateway handling millions of LLM requests - when it goes down, their entire AI stack goes down. You'll work directly with the CEO and CTO on critical projects including:

• Fixing OOM issues - e.g. Prisma Query Engine unable to recover from OOMKill in K8s deployments, unbounded in-memory buffers in spend log transactions
• Solving database connection problems - e.g. database query limits getting reached under load, spend logs loading extremely slowly, Prisma connection pool exhaustion
• Fixing race conditions and deadlocks - e.g. max_parallel_requests deadlocking API keys after provider timeouts (counter never released, Redis reset required), PodLockManager releasing another pod's lock, in-memory cache increment race conditions
• Performance optimization - e.g. update_database() doing 7 deep copies per request in the spend tracking hot path, health check fan-out overloading startup
• Improving Redis/cache reliability - e.g. budget limiter reading stale Redis data, cache sync issues between in-memory and Redis layers
• Production monitoring - making Prometheus metrics accurate (fixing missing/inf budget metrics), adding alerting, improving observability for multi-pod deployments
• Making the proxy self-healing - graceful degradation when DB/Redis is temporarily unavailable, connection retry logic, proper health checks

What is our tech stack

The tech stack includes Python, FastAPI, Redis, Postgres, Prisma ORM, Kubernetes, Prometheus, Docker.

• 1-4 years of experience running Python services in production at scale
• Experience debugging OOMs, memory leaks, connection pool issues, and race conditions
• Comfortable with PostgreSQL (query optimization, connection pooling, PgBouncer) and Redis
• Kubernetes experience - you've dealt with pod restarts, resource limits, health probes, and multi-replica coordination
• Familiarity with Prometheus/Grafana for monitoring and alerting
• Passion for open source and user engagement
• Strong work ethic and ability to thrive in small teams
• Eagerness to talk to users and help solve real problems - our GitHub issues are full of production debugging sessions and you'd be jumping into those directly

LiteLLM is the world's most popular AI Gateway used by the largest companies (Adobe, Netflix, NASA, etc.) in the world to give their developers access to LLMs and adjacent services (MCP's, Vector Stores, etc.). Why do companies use LiteLLM Enterprise Companies use LiteLLM Enterprise once they put LiteLLM into production and need enterprise features like Prometheus metrics (production monitoring) and need to give LLM access to a large number of people with SSO (secure sign on) or JWT (JSON Web Tokens)., LiteLLM (https://github.com/BerriAI/litellm) is a Python SDK, Proxy Server (LLM Gateway) to call 100+ LLM APIs in OpenAI format - [Bedrock, Azure, OpenAI, VertexAI, Cohere] and is used by companies like Rocket Money, Adobe, Twilio, and Siemens.