Senior DevOps Engineer

Who We Are:

• On a mission to ensure businesses never fail due to a third-party incident, leveraging cutting-edge AI and LLM technologies.
• Backed by tier-1 VCs, already tens of serving paying customers, and poised to triple our team in two years.
• Driven by transparency—we give top talent the full context to own their work and solve challenging problems.
• We value spontaneous discussions over formal meetings and believe working together in the same room is the fastest (and most fun) way to solve complex challenges.
• We move fast, building on a modern, scalable stack designed for high-performance data processing and ML/LLM operations—Golang, Huggingface, LangSmith, gRPC, ConnectRPC, Bufstream (Kafka-like, streaming platform), Redis, Kubernetes, Terraform, and ArgoCD.
• We heavily invest in DevEx with tools like Cursor and Okteto (read our case-study here) to ensure engineers can focus on delivering value.

Who You Are:

• A DevOps/Platform engineer with 5+ years of experience working with Kubernetes, Terraform, CI/CD, and cloud-native environments at scale.
• Eager to work with the team to define what a great AI Agent architecture looks like
• Passionate about DevEx—you see investment in developer experience as a critical enabler
• Curious and eager to adopt new technologies when they make sense - especially in the ML-Ops / LLM-Ops space.
• Have strong opinions on infrastructure design, tools, and processes

What You’ll Do:

• Own our cloud infrastructure end-to-end and shape it from ground up - from architecture and deployment to monitoring, APIs, storage, cost optimizations, security, and multi-tenancy.
• Design and evolve the platform that powers our AI agents
• Champion and implement DevEx initiatives to make every engineer on the team 10x more productive
• Collaborate with top-tier engineers who’ve built large-scale systems at top companies.

Great to Haves (Bonus Points):

• Building infrastructures from scratch.
• Direct experience building or managing infrastructure specifically for LLMs or AI agents.
• Can jump in and write production code yourself than wait around for someone else to get it done.
• Background in multi-cloud environments.
• Experience with data engineering concepts or infrastructure supporting large-scale data pipelines.