Zep is the memory and context layer for AI agents. As a Senior AI Engineer, you'll build low-latency backend systems, operate them in production on AWS, and ship LLM-powered capabilities our customers depend on.

You'll have the opportunity to work on Graphiti (25K+ GitHub stars), Zep’s popular open-source context graph framework.

This is a senior backend role centered on running LLM workloads at significant scale. We're not hiring ML researchers or data scientists. We're hiring engineers who have already lived through the messy reality of taking an LLM application from demo to production.

How we work

We're a small, distributed team that works closely together. We pair on hard problems, review each other's designs, and treat learning as part of the job rather than something that happens after hours. We ask a lot of questions: of customers, of teammates, of our own assumptions. When we find pain, we go fix it.

We expect the same back: ask questions early, push back when you disagree, and care about the people on the other end of the API.

What you'll do

• Ship product features end-to-end across backend services, APIs, data flows, and the supporting UI where it makes sense.
• Build and operate LLM-powered systems: extraction pipelines, evaluation harnesses, and reliability improvements running at scale.
• Contribute to system design for new components. Write the code, document the decisions, iterate.
• Improve production quality across performance, observability, and operational runbooks on AWS.

What we're looking for

• 6+ years of production engineering with a strong backend systems background. You've shipped services with real throughput and latency requirements.
• Master's in Computer Science or equivalent.
• Go and Python experience in real systems. You can work in critical-path code and on performance.
• Hands-on AI agent and LLM application experience. You've shipped a non-trivial agentic system to production. Not a prototype, not a thin wrapper over a chat-completion API. We expect concrete examples: multi-turn agent loops with tool calling, retrieval and context pipelines you tuned against real failures, eval harnesses you built to catch regressions, or production memory and state systems for agents.
• Working familiarity with the agent ecosystem: at least one of LangGraph, Google ADK, Mastra, or other agent SDKs, vector stores, and eval tooling.
• Extremely comfortable with spec-driven agent coding, coding harnesses, and guiding agents to build complex product.
• Hands-on AWS experience in production: deployments, monitoring, scaling, cost and reliability tradeoffs.

Nice to have

• TypeScript experience for frontend or SDK work.

Tech stack: Go, Python, TypeScript, AWS.

This role is probably NOT a fit if:

• Your LLM experience is single-turn chat completions or RAG-as-a-feature.
• Your background is primarily in ML research or model training rather than shipping agent systems in production.
• You haven't operated production backend systems with real latency or throughput requirements.