Akhilesh Yelamali profile photo 1 on Tal

Akhilesh Yelamali

Embedded Systems | Co-Author of a research paper | Edge AI | RTOS | QNX

Bengaluru

1+ year of work experience

Sasken Technologies Limited
Feb 20267 moPresent

Bengaluru

NVIDIA
Feb 20246 mosJul 2024

Remote

• Developed QNX Resource Manager Monitor for real-time health monitoring and anomaly detection in QNX OS-based embedded systems, achieving 99.8% uptime and supporting 10+ Resource Modules with 10-second health metric updates. • Optimized watchdog system with a 5-second heartbeat interval, resulting in 50% faster recovery times for detected faults, and leveraged Genetic Algorithms to reduce CPU usage by 20% while improving data processing times by 15%. • Enhanced resilience through fault-detection algorithms and heuristic optimizations, achieving 78% accuracy in anomaly detection and reducing system overhead by 25%.
KLETech-Center of Excellence in Visual Intelligence (CEVI)
Oct 20231 yr 2 mosNov 2024

On-site

Akhilesh’s AI Stack

claude code
claude code
claude
claude
cursor
cursor
gemini
gemini
hugging face
hugging face
llama.cpp
llama.cpp

Education

KLE Technological University - Hubballi (India)

Bachelor of Engineering - BE, Electronics and communication

KLE Technological University - Hubballi (India)

20214 yrs2025

Sir MV PU College, Davangere

20192 yrs2021

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Akhilesh Yelamali on Tal

Headline

Embedded Systems | Co-Author of a research paper | Edge AI | RTOS | QNX

Location

Bengaluru

AI stack

claude code

claude

cursor

gemini

hugging face

llama.cpp

Work experience

Software Engineer at Sasken Technologies Limited, Feb 2026 - Present · 7 mo, Bengaluru

Project Developer (NVATIC) at NVIDIA, Feb 2024 - Jul 2024 · 6 mos, Remote, • Developed QNX Resource Manager Monitor for real-time health monitoring and anomaly detection in QNX OS-based embedded systems, achieving 99.8% uptime and supporting 10+ Resource Modules with 10-second health metric updates. • Optimized watchdog system with a 5-second heartbeat interval, resulting in 50% faster recovery times for detected faults, and leveraged Genetic Algorithms to reduce CPU usage by 20% while improving data processing times by 15%. • Enhanced resilience through fault-detection algorithms and heuristic optimizations, achieving 78% accuracy in anomaly detection and reducing system overhead by 25%.

Project Intern at KLETech-Center of Excellence in Visual Intelligence (CEVI), Oct 2023 - Nov 2024 · 1 yr 2 mos, On-site

Education

Bachelor of Engineering - BE, Electronics and communication, KLE Technological University - Hubballi (India)

Sir MV PU College, Davangere

Akhilesh Yelamali - Software Engineer in Bengaluru | Tal