Design and build scalable, fault-tolerant infrastructure systems that power Harvey's AI platform across multiple cloud regions
Own and evolve our multi-cloud infrastructure (Azure, GCP), including Kubernetes orchestration, networking, and container management
Lead technical initiatives around observability, incident response, and operational excellence — building systems that enable rapid detection and resolution of issues
Architect and optimize our distributed systems for reliability, including load balancing, quota management, and failover mechanisms
Partner with Product Engineering and Security teams to ensure our infrastructure is an accelerant, not a constraint
Drive infrastructure-as-code practices using tools like Terraform and Pulumi to enable reproducible, auditable deployments
Mentor junior / intern engineers and raise the technical bar across the organization through code reviews, design reviews, and technical leadership
Design and implement a next-generation model proxy architecture that routes millions of daily inference requests while maintaining model API compatibility and enabling seamless model integration
Build distributed rate limiting and quota management systems using Redis-backed algorithms to handle bursty traffic patterns without degrading user experience
Architect multi-region deployment strategies that meet strict data residency requirements for global enterprise customers
Develop comprehensive observability infrastructure with granular SLA monitoring, burn rate alerts, and detailed token attribution for cost tracking
Lead the evolution of our CI/CD pipelines to improve developer velocity while maintaining production stability