Infrastructure Engineer

Kastle is building the AI employee for financial services. We work with some of America's largest banks, helping them scale their operations using autonomous AI Agents that process payments, originate loans, and help customers get a world class experience during some of the biggest financial decisions of their lives. We've processed over $2 billion in transactions and are backed by Insight Partners and Y Combinator. If you are interested in building AI that is applied in the real world where precision, reliability, and performance matter and redefine access to credit across the world, apply below. We just raised a $24M Series A and are growing fast! What We’re Looking For: At Kastle our Infrastructure team is responsible for building the underlying systems that help thousands of people daily, make their largest and most important financial transactions. We're looking for a Senior Infrastructure Engineer with deep distributed systems expertise to design, build, and operate the infrastructure that powers Kastle's AI agents in production at banks, lender and other financial institutions. You'll work on the hard problems of distributed systems — consistency, fault tolerance, horizontal scalability, and low-latency coordination — across AWS/GCP/Azure, Kubernetes, and Terraform, keeping compliance-critical systems reliable as we grow. This is an onsite role working closely with platform, ML, and security engineering. 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