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johnquevedo/README.md

John Quevedo | Software Engineer

I'm a software engineer with hands-on experience building scalable backend systems and AI applications. I focus on backend engineering, distributed systems, and machine learning.

  • 🎓 Computer Science and Mathematics at Yale University
  • 💻 Interests: Backend Engineering, Distributed Systems, and Machine Learning
  • 🌱 Currently learning: System Design, Cloud-Native Architecture, and ML Deployment
  • 📫 Reach me: john.quevedo@yale.edu

Technical Skills

  • Languages: Python, Java, TypeScript, C++, Go, SQL
  • Frameworks & Tools: Spring Boot, React, Node.js, Express, PyTorch, Hugging Face, FastAPI, Docker, Git
  • Databases & Cloud: PostgreSQL, Redis, AWS (Lambda, Bedrock, CDK)
  • Other: REST APIs, WebSockets, RAG, MCP, Linux

📂 Featured Projects

Project Description
Distributed CI Failure Replay Platform
  • Reproduced 172/172 controlled CI failures while scaling isolated execution to 50 concurrent jobs and 18.5 jobs/second.
  • Preserved tenant isolation and artifact integrity through worker crashes, PostgreSQL restarts, corrupted artifacts, duplicate webhooks, retries, and dead-letter recovery.
Production Data Repair Engine
  • Restored 100,000 corrupted payment records at 865 records/second alongside 8,787 concurrent writes, with zero duplicate applications, invalid payments, unbalanced ledgers, or incorrect fees.
  • Preserved repair correctness through worker termination, Kafka/PostgreSQL restarts, Kubernetes pod deletion, duplicate events, validation failures, and concurrent-write conflicts.
Training Data Quality System
  • Achieved 91.3% adjudicated precision in a blinded 100-item independent review by prioritizing label errors, duplicates, and leakage using kNN-Shapley, abstaining weak supervision, and diversity-aware selection.
  • Audited 140,683 examples across BANKING77 and AG News, surfaced 243 duplicate groups and 17 cross-split overlaps, and opened a reproducible upstream BANKING77 correction PR.
Hardware Aware Inference Optimizer
  • RReduced median batch-one latency 79.6% and deployable artifact size 21.0% with 0.0 pp quality loss through hardware-aware Pareto optimization.
  • Completed 60/60 NVIDIA T4 trials across three pretrained models and two physical targets, selecting and exporting deployable configurations using CUDA, TensorRT 11, ONNX Runtime, and Ray.

📊 Github Highlights

  • Maintain 16 public repositories spanning open-source infrastructure, machine learning systems, academic work, and personal projects.
  • Shipped 4 production-oriented open-source systems with versioned releases, automated CI, reproducible benchmarks, containerized testing, and documented failure analysis.
  • Published reusable developer infrastructure through the GitHub Actions Marketplace.

📫 Connect with Me

Pinned Loading

  1. hardware-aware-inference-optimizer hardware-aware-inference-optimizer Public

    Evidence-first hardware-aware optimization across PyTorch, CUDA, ONNX, TensorRT, and Ray

    Python

  2. production-data-repair-engine production-data-repair-engine Public

    Production-safe correction of corrupted payment records with Java, Kafka, PostgreSQL, Spark, and Kubernetes

    Java

  3. reproci reproci Public

    Capture and replay pytest CI failures with content-addressed artifacts and isolated sandboxes.

    Python

  4. training-data-quality-system training-data-quality-system Public

    Explainable, local-first training-data quality and debugging for dataset maintainers

    Python