Jesse Hernandez

About Jesse Hernandez

I’m an experienced Sr Quality Assurance Engineer with 15+ years of proven success in mobile, web, and embedded systems testing across consumer electronics and automotive tech. My expertise has evolved from building robust QA frameworks to focusing on AI model evaluation, prompt testing, behavior auditing, and multi-agent workflows. Leveraging my background in QA to bring rigor, repeatability, and transparency to LLM performance analysis. I’m certified in Google Cloud AI Agents, Berkeley Data Analytics and Machine Learning and experienced in validating AI products across cloud and edge environments.I’m passionate about bridging the gap between traditional QA engineering and the rapidly evolving world of LLMs—helping teams understand how models behave, where they fail, and how to improve their reliability at scale.Core Strengths:• AI model validation • LLM behavior & prompt evaluation• Automation frameworks & Python-based tooling• Observability and drift detection• REST API, data pipeline, and cloud integration testing• Cross-functional collaboration between QA, data, and ML teamsTechnical Stack:Python · OpenAI API · RAG · Ollama · PyTest · REST API Testing · Model Drift Detection · Databricks · AWS · GCP · Docker · Kibana · Tableau · CI/CD · GitHubIf you’re working on AI systems that need to be tested, validated, or better understood, I love collaborating on projects where reliability, safety, and user trust truly matter.

Experience

1. Sr Software Qa Engineer

   Nauto

   Sep 2017 — Present · Palo Alto, CA, US

   Boosted AI/perception Distraction model’s state-machine quality by analyzing the event-rate of event-detection performance across v1 and v2 releases resulting in a No-Go decision to release that would have caused Fleet Vera Score spikes outside of acceptable ranges- Collaborate with Data Science to uncover algorithm discrepancies in our edge device implementation that resulted in a more accurate triggering of our Forward collision alerts- Built the Vera Score Dashboard and Device Health Dashboard to detect performance drift and error rates across pre-release and GA versions, preventing metric degradation in fleet deployments- Automated data ingestion for 10K+ edge devices, enabling large-scale testing and monitoring of edge device health and model performance consistency- Delivered a seamless proprietary Device Config Tool v1 to v2 migration with zero production downtime by hardening pre-prod environments and monitoring for regression- Oversaw the development of our OTA server system with our outsourced team to ensure that our AOSP firmware reliably updates our devices under various failure conditions preventing regressions and ensuring stable deployments to live fleets.

Education

• University of California, Berkeley

  Certificate in Data Analytics, Data Analytics

• ITT Technical Institute

  Bachelors of Science, EE/Automated Manufacturing Technology

Skills

• Windows
• Linux
• Operating Systems
• Quality Assurance
• Active Directory
• Perl
• Tcp/Ip
• Vmware
• Xml
• Telecommunications
• Databases
• Software Quality Assurance
• Troubleshooting
• Technical Support
• Apache
• Integration
• Testing
• Sql
• Shell Scripting
• Os X
• Test Cases
• Ubuntu
• Hardware
• Android
• Java
• Jira
• Agile Methodologies
• Mobile Applications
• Mobile Devices
• Unix
• Test Planning
• Web Applications
• Python
• Javascript
• Test Automation