Senior Backend Engineer

Reposted 2 Days Ago
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San Francisco, CA, USA
In-Office
200K-260K Annually
Senior level
Artificial Intelligence • Machine Learning • Natural Language Processing • Software
The Role
The Senior Backend Engineer will design and scale backend guardrails, enhance observability tools, ensure error transparency, and maintain compliance for high-volume traffic systems.
Summary Generated by Built In

Senior Backend Engineer

LiteLLM is the world's most popular AI Gateway, trusted by top companies like Adobe, Netflix, and NASA. Our platform empowers developers by providing secure, reliable access to LLMs and adjacent services, and we're looking for a Senior Backend Engineer to help us build rock-solid guardrails and observability tooling at scale.

About The Role
You’ll focus on owning our guardrails and logging world-class. You will be in charge of the backend code that ensures all guardrail calls are consistently logged, errors are surfaced to users (not silently swallowed), and our observability instrumentation works for real-world, high-volume traffic. Your attention to detail in areas like latency metrics, logging traceability, and backend guardrail registration will directly impact user trust in our security and compliance features.

Responsibilities

  • Build and scale our product, ensuring performance, reliability, and continuous improvement.

  • Ensure all guardrail and policy enforcement calls (e.g., applyguardrail) are properly logged and traceable through our SpendLogs and relevant database tables

  • Build and design CPU-level guardrails to cover common attacks on LLM API's / MCP servers / Agents

  • Identify and fix areas where silent failures occur in guardrail creation, registration, and policy application—ensuring robust error handling and transparency to end users

  • Work with observability integrations, including Datadog, Splunk, Prometheus, and OpenTelemetry, to maintain accurate, configurable, and usable monitoring and logging for backend systems

  • Enhance observability integrations to work for 1B+ requests/mo., with minimal latency overhead and no memory leaks (e.g. due to cardinality of Prometheus metrics)

  • Collaborate cross-functionally on backend engineering priorities (performance, reliability, security)

What We’re Looking For

  • Bachelor’s or Master’s in Computer Science or related field

  • 1+ years of experience with Python and backend frameworks (e.g. FastAPI, Flask)

  • Understanding of logging best practices, error handling, and secure backend development

  • Exposure to monitoring, logging, or metrics platforms (Datadog, Splunk, Prometheus, OpenTelemetry)

  • Familiarity with database integration and troubleshooting (PostgreSQL, Redis, etc.)

  • Driven to deliver high-quality backend code with strong guardrails, auditing, and debugging capabilities

  • Eagerness to tackle hard bugs and ensure system transparency for end users

Why Join LiteLLM?

  • High-impact, mission-critical work on the core of compliance and reliability

  • Contribute directly to features used by enterprise customers at global scale

  • Fast-paced growth environment with room for technical ownership

  • Competitive salary, health, dental, and vision benefits

About LiteLLM
LiteLLM (https://github.com/BerriAI/litellm) is a Python SDK and Proxy Server enabling seamless calls to 100+ LLM APIs in the OpenAI format, trusted by industry leaders worldwide.

Ready to shape the future of secure, observable AI infrastructure? Apply now!

Skills Required

  • 4+ years of experience with Python and backend frameworks (e.g. FastAPI, Flask)
  • Understanding of logging best practices, error handling, and secure backend development
  • Exposure to monitoring, logging, or metrics platforms (Datadog, Splunk, Prometheus, OpenTelemetry)
  • Familiarity with database integration and troubleshooting (PostgreSQL, Redis, etc.)
Am I A Good Fit?
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The Company
13 Employees
Year Founded: 2023

What We Do

LiteLLM is an open-source AI Gateway that provides a unified interface to access over 100 LLM providers, simplifying model access, spend tracking, and fallbacks.

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