Harsha Sikha
Data Scientist | Machine Learning Engineer | Generative AI Specialist | Driving Intelligent Automation, Predictive Insights & Scalable AI Solutions
- Role
- Embedded Software Engineer at IBM
- Location
- Jersey City, NJ, US
- LinkedIn followers
- 500 followers
About Harsha Sikha
I’m an AI/ML Engineer with 3+ years of experience building production-ready AI, ML, and…
Experience
Embedded Software Engineer
Feb 2025 — Present · NJ, US
Designed and implemented firmware in C and Embedded C++ for ARM Cortex-M microcontrollers (STM32, MSP430), focusing on optimizing execution speed and memory efficiency, which improved performance of real-time acquisition modules used in industrial test benches.• Developed multitasking applications on FreeRTOS, configuring timers, semaphores, and ISR routines to manage concurrent sensor fusion tasks, ensuring 98% accurate scheduling and significantly reducing latency in closed-loop motor control systems.• Integrated UART, SPI, and I2C communication drivers across multiple peripheral boards, performing signal-level validation on oscilloscopes to guarantee reliable throughput, which increased data transfer performance in lab prototypes by 20%.• Customized Yocto build framework and U-Boot bootloader for embedded Linux systems, enabling smooth bring-up of new TI-based boards and accelerating BSP integration timelines during new hardware releases.• Troubleshot complex issues using GDB and JTAG debuggers, along with logic analyzer traces, to isolate firmware bottlenecks and critical timing faults, leading to a 30% reduction in system crashes reported from field units.• Configured GitLab CI pipelines for automated builds, static analysis, and unit testing, which eliminated repetitive manual validation and reduced integration overhead across the team by 40%.• Built lightweight SQLite data logging modules to store 24/7 telemetry data locally and linked them with Grafana visualization dashboards, enabling engineers to remotely track and analyze hardware behavior in production.• Collaborated with product owners and hardware engineers under Agile Scrum methodology, actively participating in sprint planning, backlog refinement, and reviews, maintaining a consistent 95% release-on-time record.
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