TODAY
Automation and validation leadership
Architecting full-stack simulator platforms, scalable automation frameworks, distributed service validation, cloud delivery pipelines, and OTA release systems.
ABOUT AUTORIGOR AI
Senior engineering, without the layers.
AutoRigor AI is a specialist engineering practice for teams whose software crosses boundaries: browser to API, service to cloud, test bench to vehicle network, and firmware to production.
Start a technical conversationENGINEERING PROFILEAR–ABOUT
8+years of hands-on software, systems, and validation experience
FirmwareCloud
Engineering by trainingAdvanced study in computer engineering, grounded in electrical engineering.
WHY AUTORIGOR AI
Built for problems that refuse to stay in one lane.
The practice combines software development and verification thinking. That means a simulator is designed to be operable, an API is designed to be testable, and an automation framework is treated like a product—not a pile of scripts.
EXPERIENCE, TRANSLATED
A career spent connecting the layers.
The foundation spans validation, embedded systems, full-stack development, distributed services, cloud delivery, and connected vehicle software.
EMBEDDED
Firmware and device engineering
Developing C/C++ software for Linux-based devices, debugging communication protocols, automating delivery, and owning the firmware lifecycle from implementation to production.
FOUNDATION
System verification
Building regression frameworks, creating risk-based test plans, improving coverage, and investigating system performance at the boundary between software and hardware.
WORKING RANGE
FULL-STACK SYSTEMS
C#, .NET, React, REST, WebSockets, Docker, Azure, SQL, MongoDB, and Redis.
QUALITY AUTOMATION
Python, pytest, Selenium, Appium, API testing, system validation, Jenkins, and GitHub Actions.
CONNECTED SOFTWARE
CAN, J1939, OBD-II, CAPL, embedded Linux, C/C++, device protocols, and OTA systems.
OPERATING PRINCIPLES
How the work gets done.
See the whole system
The most expensive failures live between components, disciplines, and ownership boundaries.
Prove before promising
Working increments, observable behavior, and automated evidence make decisions easier.
Leave capability behind
Documentation, maintainable architecture, and knowledge transfer are part of the deliverable.
A PRACTICAL FIRST STEP