3 days ago
Base Salary
$180k - $275k/yr
Responsibilities
- Design and operate distributed systems powering production AI agents.
- Architect service communication, coordination, state management, replication, failover, backups, and disaster recovery across regions and availability zones.
- Own Kubernetes cluster architecture, scaling, upgrades, networking, and security hardening.
- Build and maintain Terraform modules and repeatable infrastructure change pipelines.
- Diagnose distributed-systems failures including network partitions, cascading failures, split-brain scenarios, and race conditions.
- Build observability through metrics, distributed tracing, logging, and alerting.
- Partner with ML, platform, and security engineering on capacity planning, consistency guarantees, and architecture reviews.
- Participate in production infrastructure on-call and incident response.
- Mentor engineers and review distributed-systems architecture and infrastructure-as-code changes.
Requirements
- 5+ years of experience in infrastructure, platform, or backend engineering roles with substantial hands-on distributed systems experience.
- Strong understanding of consensus, replication, partitioning, consistency models including CAP and PACELC, and distributed-systems failure modes.
- Production experience with at least one major cloud provider at scale: AWS, GCP, or Azure.
- Hands-on production Kubernetes experience including cluster upgrades, networking, and security policy.
- Experience with distributed data stores, message queues, or event-streaming systems such as Kafka, etcd, Cassandra, or CockroachDB.
- Strong infrastructure-as-code background, preferably with Terraform, and experience with CI/CD pipelines.
- Programming or scripting proficiency in Go, Python, or Rust for building distributed tooling.
- Experience owning production reliability through on-call, incident response, and postmortems.
- Ability to work onsite and collaborate with product, ML, and security teams.
Benefits
- Onsite work arrangement.
- Opportunity to build production AI infrastructure for major financial institutions in a compliance-critical environment.
- Collaboration with platform, ML, and security engineering teams.
Tech Stack
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