Together AI Resume Example
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Together AI resume sample
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Together AI resume example (text version)
Sofia Martinez · Together AI
Summary
Senior Software Engineer with 7 years of experience designing high-performance distributed systems in Golang and Python. Orchestrated global cloud infrastructure using Kubernetes and Terraform to support sub-millisecond latency for large-scale production environments. Implemented observability stacks with Prometheus and Grafana, resulting in a 40% reduction in mean time to resolution for critical incidents. Architected low-latency VPC networking for GPU clusters, optimizing data throughput for distributed AI model training across three regional data centers.
Experience
Senior Software Engineer · Stripe
South San Francisco, CA · 2023 - Present
- Designed a globally distributed Golang service architecture that handled 500k+ requests per second
- Managed 150+ Kubernetes clusters across three AWS regions using Terraform and GitOps workflows
- Configured high-performance networking via SR-IOV to improve data transfer speeds by 25% for payment processing nodes
- Developed custom Prometheus exporters to track resource utilization of GPU nodes in AI training clusters
- Mentored four junior engineers through technical design documentation reviews and code quality audits
Software Engineer · Datadog
New York, NY · 2021 - 2023
- Built distributed tracing microservices in Golang to monitor system dependencies at scale
- Reduced infrastructure costs by 18% through the implementation of automated right-sizing scripts for EC2 instances
- Deployed a highly available monitoring pipeline processing 10TB of telemetry data daily
- Integrated custom Grafana dashboards for executive reporting, increasing transparency on system uptime
- Collaborated with SRE teams to automate failover procedures, reducing downtime incidents by 30%
Infrastructure Engineer · HashiCorp
San Francisco, CA · 2019 - 2021
- Maintained core infrastructure-as-code modules for enterprise clients using Terraform and Packer
- Optimized QEMU-based virtualization layers for high-density compute nodes in internal data centers
- Authored technical specifications for cloud networking and VPC peering across complex hybrid environments
- Implemented automated testing for infrastructure code, resulting in a 50% decrease in manual deployment errors
- Supported global scaling initiatives by streamlining the deployment of HashiCorp stack clusters
Associate Software Engineer · Twilio
San Francisco, CA · 2019 - 2019
- Developed backend API components in Golang for internal communication management tools
- Optimized database queries for high-volume logs, reducing response times by 15%
- Supported the migration of legacy services to containerized environments using Docker and Kubernetes
- Contributed to CI/CD pipeline automation for faster production deployments
- Resolved critical bugs in system networking layers, improving overall platform stability
Education
MS in Computer Science · Stanford University
Stanford, CA · 2017 - 2019
- Specialized in Distributed Systems and Cloud Computing architectures
- Conducted research on latency optimization in virtualized network environments
- Graduated with 3.9 GPA and received departmental honors for thesis on container orchestration
BS in Electrical Engineering and Computer Science · University of California, Berkeley
Berkeley, CA · 2013 - 2017
- Completed rigorous coursework in Operating Systems and Data Center Networking
- Led undergraduate capstone project on automated infrastructure provisioning
- Awarded Dean's List honors for four consecutive semesters
Skills
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Together AI resume summary example
Senior Software Engineer with 7 years of experience designing high-performance distributed systems in Golang and Python. Orchestrated global cloud infrastructure using Kubernetes and Terraform to support sub-millisecond latency for large-scale production environments. Implemented observability stacks with Prometheus and Grafana, resulting in a 40% reduction in mean time to resolution for critical incidents. Architected low-latency VPC networking for GPU clusters, optimizing data throughput for distributed AI model training across three regional data centers.
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