WHAT YOU’LL DO
Design and operate distributed systems that power AI agents in production, with an emphasis on consistency, fault tolerance, and horizontal scalability
Architect service-to-service communication, coordination, and state management for systems spanning multiple regions and availability zones
Own Kubernetes cluster architecture, scaling, and security hardening across environments
Build and maintain Terraform modules and GitOps pipelines so infrastructure changes are reviewed, versioned, and repeatable
Design for the reliability and auditability bar that regulated financial institutions require: replication, failover, backups, disaster recovery, and change logs
Diagnose and resolve distributed-systems failure modes — network partitions, cascading failures, split-brain scenarios, race conditions — under production pressure
Build observability (metrics, distributed tracing, logging, alerting) so the team can see problems before customers do
Partner with ML, platform, and security engineering on capacity planning, data consistency guarantees, and architecture reviews
Participate in an on-call rotation for production infrastructure
Mentor other engineers on distributed systems design and review their architecture and IaC changes
WHAT WE’RE LOOKING FOR
5+ years in infrastructure, platform, or backend engineering roles, with substantial hands-on distributed systems experience
Strong understanding of distributed systems fundamentals: consensus, replication, partitioning, consistency models (CAP/PACELC), and failure modes
Production experience with at least one major cloud provider (AWS, GCP, or Azure) at scale
Hands-on experience running Kubernetes in production, including cluster upgrades, networking, and security policy
Experience designing or operating distributed data stores, message queues, or event streaming systems (e.g. Kafka, etcd, Cassandra, CockroachDB)
Strong background in infrastructure-as-code (Terraform preferred) and CI/CD pipelines
Proficiency scripting/programming in at least one language (Go, Python, or Rust) for building distributed tooling
Experience owning production reliability: on-call, incident response, postmortems
Comfortable working onsite and collaborating closely with product, ML, and security teams
