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Boban Raphael

@bobanraphael

Backend software engineer at Globus Medical who cut image-processing latency by 40% and moved desktop systems to cloud deployment.

Canada

Message

At Globus Medical, I engineered high-throughput C++ image-processing pipelines that cut latency by 40%, and architected containerized microservices to move a multi-platform desktop architecture to the cloud.

I also refactored the ExcelsiusXR AR headset UI to raise test coverage from 10% to 50%, and added crash-dump logging that cut debugging time from days to hours. Previously, I built backend services and Python data pipelines at Inspirein Technologies, including AWS ingestion for fleet telemetry.

Experience

Work history, roles, and key accomplishments

Current

Software Engineer

Current

Globus Medical

Feb 2024 - Present (2 years 8 months)

Sustained production reliability and engineered high-throughput C++ image-processing pipelines, cutting latency by 40%. Architected containerized microservices and refactored legacy C# code, raising test coverage from 10% to 50%.

C++C#Docker Kubernetes Microservices CI CD GitHub Actions Testing Code Review AWS

Software System Design Engineer

Analog Devices

Dec 2020 - Nov 2023 (2 years 11 months)

Engineered core backend service libraries across the full SDLC, meeting low-latency, high-reliability standards. Built multi-stage Jenkins CI/CD pipelines and contributed debugging features to OpenOCD.

C++Jenkins CI CD Linux Windows Agile Scrum Unit Testing Integration Testing OpenOCD

Software Engineer Intern

DJ Instruments

Jul 2019 - Dec 2019 (5 months)

Co-developed hardware/software data acquisition components and peripheral drivers for pressure-sensor platforms. Integrated ADC telemetry and I2C communication on Renesas microcontrollers.

C I2C ADC Renesas Embedded Systems Data Acquisition

Software Engineer

Inspirein Technologies

Jul 2016 - Jun 2018 (1 year 11 months)

Built backend services and distributed Python data pipelines for real-time and scheduled batch processing of fleet telemetry. Designed AWS cloud data ingestion with IoT telemetry into DynamoDB via MQTT.

Python AWS DynamoDB MQTT IoT Data Pipelines Jupyter