Sandip SitaulaManufacturing Test & Systems Integration Engineer
Test and systems-integration engineer with 6+ years of experience developing, integrating, validating, and sustaining hardware/software test solutions for high-volume electronics operations. Based in Fort Worth, Texas, with experience in Python automation, test fixtures, machine vision, RFID, SQL, and MES integration.
Engineering for a better test line.
I develop solutions that make manufacturing testing more efficient and support quality on the production line. My work connects hardware, software, and automation: preparing devices for testing, inspecting packing quality, automating warehouse cycle counts, and improving the test process.
My experience also includes working with large language models (LLMs).
Selected engineering outcomes
- >800×
Charging throughput
Reconext · Automated charging-station platform deployed across global facilities.
- 60% → 85%
First-pass yield
Reconext · Fixture screening and test-process improvements.
- 600
Validation runs
Reconext · YOLO packout inspection validation before production release.
- 50%
Less diagnostic time
Reconext · Bounce Management System connecting complaints, repair history, and test results.
Tools meet the process.
- Hardware/software systems integration
- Manufacturing test engineering
- Test system validation and debugging
- Verification and validation support
- Automated test equipment and fixtures
- Component-level PCB troubleshooting
- Failure analysis and root-cause investigation
- Python test automation
- YOLO, OpenCV, and machine vision
- RFID, barcode/QR readers, and sensors
- MES integration and traceability
- SQL Server and MySQL
- PHP, JavaScript, and REST APIs
- Power BI and engineering analytics
- CSV, CCR, and ISO 13485 documentation
- Git/GitHub and technical documentation
- Cross-functional and global team coordination
- Large language models (LLMs)
Where the work happens.
- Jun 2021 – Present
Manufacturing Test & Process Development Engineer
ReconextGrapevine, TX
- Lead development, integration, validation, deployment, and optimization of test processes, fixtures, diagnostic applications, and automated equipment for electronics refurbishment and NPI programs.
- Integrate cameras, barcode/QR readers, RFID systems, sensors, charging systems, Python applications, SQL databases, and MES interfaces for automated testing, data capture, and operator guidance.
- Partner with customer engineering, quality, operations, contract manufacturers, and global teams on NPI and pilot builds; supported integration and validation of repair and test processes that achieved 94% pilot yield.
- Execute structured validation activities, including 600 runs for a YOLO-based packout inspection solution before production release.
- Investigate hardware, software, fixture, firmware, equipment, and process failures; corrective actions reduced station downtime by 25%. Python bench diagnostics increased repair yield by 15%.
- Improve fixture screening and test processes, increasing first-pass yield from 60% to 85%. Design and deploy a charging-station platform that increased charging throughput by more than 800×.
- Build MES process controls, traceability workflows, SQL/PHP/Power BI analytics, and an integrated Bounce Management System that reduced engineering diagnostic time by 50%.
- Support computer-system validation, Change Control Requests, software validation, preventive maintenance, and engineering documentation within an ISO 13485 environment.
- Dec 2018 – Jun 2021
Engineering Technician
CTDIFlower Mound, TX
- Supported development, validation, debugging, and sustainment of production test systems with design, test, and validation engineers.
- Performed component-level PCB troubleshooting and repair, isolated recurring hardware and equipment failures, and supported corrective actions.
- Analyzed station failures, verified repair effectiveness, and provided hands-on support for production flow and quality.
- Led PCB repair enablement and operator training, translating technical methods into repeatable procedures and work instructions.
Practical work. Purpose-built systems.
Automated Device Charging Stations for Manufacturing Test
At Reconext, I designed and deployed an automated charging-station platform that integrates hardware, control software, data capture, and production workflows. Once a device is connected, the station handles charging to the level required for testing.
The platform increased charging throughput by more than 800× and was deployed across global facilities, reducing the need for repeated hands-on checks during device preparation.
https://sannep.com/projects/automated-charging-stations/YOLO Computer Vision for Device Packing Quality Inspection
I developed a YOLO-based automated inspection solution and executed 600 structured validation runs before production release. The validation work targeted zero tolerance for missed items in the packout process.
The inspection solution completed 600 validation runs before release, supporting automated accessory and component-presence checks as part of the manufacturing quality process.
https://sannep.com/projects/yolo-vision-quality-inspection/RFID Inventory Automation, WIP Tracking & Sequence Control
I developed an RFID solution for automated warehouse cycle counts. In a separate WIP and sequence-control platform, I integrated RFID readers with manufacturing data to track device journeys, WIP aging, zone duration, throughput, and sequence compliance in real time.
The warehouse solution automates cycle counting. The manufacturing platform provides real-time WIP visibility and sequence-compliance tracking to support production control and investigation.
https://sannep.com/projects/rfid-warehouse-inventory/Manufacturing Test Software, Diagnostics & Engineering Analytics
I develop Python bench diagnostics and MES controls that enforce station sequence and required operations. I also built SQL, PHP, and Power BI analytics linking test and repair data, plus a Bounce Management System that combines complaints, vendor information, repair history, and return-test results.
Python bench diagnostics increased repair yield by 15%. The separate Bounce Management System reduced engineering diagnostic time by 50% and improved visibility into recurring failure trends.
https://sannep.com/projects/manufacturing-test-software/The foundation behind the work.
Master of Science in Computer Science
University of the Cumberlands
Bachelor of Science in Software Engineering
The University of Texas at Arlington
Large language models.
Hands-on experience with LLMs, alongside my work in manufacturing test engineering, automation, and computer vision.