Carlos Chulo

Lead infrastructure architect for ML and data platform teams, covering MLOps tooling, scalable data pipelines, and cloud infrastructure across AWS, GCP, and Azure at genomics scale. Owned ML lifecycle management and automated deployment of recommendation systems, cutting release time from 3 hours to under 20 minutes.

Owned DevOps and backend engineering for the sequencing data platform, running CI/CD, container infrastructure, and cloud environments across a team of roughly 12 engineers. Migrated 14 services to Kubernetes and built ML-specific data infrastructure, reducing P99 latency from over 600 ms to under 400 ms.

Backend and infrastructure engineer embedded with the data platform team, focused on data pipeline reliability and cloud deployment for genomics data processing services. Built data pipelines in Python handling roughly 2 TB per day and containerized legacy services with Docker.

Short-term senior contributor on internal DevOps tooling for semiconductor design automation, focused on CI pipeline reliability and environment management. Stabilized a flaky Jenkins-based CI pipeline by rewriting key bash scripts and documented deployment procedures for 5 internal tools.

Software and DevOps engineer supporting internal simulation and test infrastructure for RF semiconductor design teams, working across Linux, Python, and shell tooling. Automated data collection from simulation runs and set up Docker-based local development environments.

Bachelor's degree in Computer Science from the University of California, San Diego, completed in 2018.