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This is a founding engineering role at a seed-stage AI infrastructure startup building a production-grade LLM gateway that sits at the core of AI-first products. You will work directly with the founders to shape the technical direction and architecture of the platform, making a tangible impact from day one. If you want to leave a real mark on a fast-growing company, or are building toward founding your own startup someday, this role is a rare opportunity to do meaningful work at the ground floor.
This is a founding engineering role at a seed-stage AI infrastructure startup building a production-grade LLM gateway that sits at the core of AI-first products. You will work directly with the founders to shape the technical direction and architecture of the platform, making a tangible impact from day one. If you want to leave a real mark on a fast-growing company, or are building toward founding your own startup someday, this role is a rare opportunity to do meaningful work at the ground floor.
Design and build backend services that power an LLM routing and observability platform serving production AI workloads.
Own architectural decisions across a pragmatic microservices stack, balancing scalability with the practical needs of an early-stage product.
Contribute to the core gateway service written in Go, as well as auxiliary and ML/AI services in Python.
Select and apply the right database and caching technologies for each problem, across SQL databases and distributed caches.
Work within and evolve a Kubernetes-based infrastructure, redefining the stack as the product grows.
Collaborate closely with the founding team to translate product goals into robust, efficient engineering solutions.
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5 or more years of experience building backend services in a compiled language, with strong preference for Go.
Solid, practical understanding of concurrency and demonstrated experience applying it in production systems.
Hands-on experience selecting and using different database and caching technologies appropriately.
Ability to design architectures that are both scalable and pragmatic, calibrated to where a product actually is.
Strong judgment around tradeoffs between quality, speed, and completeness when building minimal but correct products.
A track record of independently picking up new technologies and running with them.
Experience with Kubernetes is a strong plus.
Background in AI or LLM infrastructure, or developer tooling, is a plus.
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