Anuj Agrawal

Software Engineer

Bengaluru, Karnataka, India6 yrs 2 mos experience

Key Highlights

3 years of experience in embedded software development.
Expertise in firmware development and testing.
Proficient in multiple programming languages including C, C++, and Python.

Stackforce AI infers this person is a skilled Embedded Software Engineer with a focus on Automotive and IoT solutions.

Contact

Skills

Core Skills

Embedded SoftwareFirmware Development

Other Skills

QNXOTAC++Software DevelopmentObject-Oriented Programming (OOP)Kernel DriversMicrocontrollersIntegration TestingBootloaderDebuggingRust (Programming Language)Verification and Validation (V&V)Joint Test Action Group (JTAG)ProgrammingVirtualization

About

Embedded Software Developer with 3 years of experience specialized in Firmware development and testing. Experienced with all stages of the development cycle for dynamic Embedded projects. Well-versed in numerous programming languages including C, C++, Python.

Experience

6 yrs 2 mos

Total Experience

1 yr 2 mos

Average Tenure

Current Experience

Stellantis

Software Lead

Jan 2024 – Sep 2025 · 1 yr 8 mos · Bengaluru, Karnataka, India · Hybrid

QNXOTAC++Embedded SoftwareFirmware Development

Alphawave semi

Senior Firmware Engineer

Dec 2022 – Nov 2023 · 11 mos · On-site

Software DevelopmentObject-Oriented Programming (OOP)Kernel DriversMicrocontrollersIntegration TestingBootloader+13

Intel corporation

Firmware Engineer

Feb 2022 – Dec 2022 · 10 mos · Bengaluru, Karnataka, India

Software DevelopmentObject-Oriented Programming (OOP)Kernel DriversMicrocontrollersIntegration TestingBootloader+16

Qualcomm

Embedded Software Engineer

Mar 2021 – Feb 2022 · 11 mos · Banglore

Software DevelopmentObject-Oriented Programming (OOP)BSPKernel DriversMicrocontrollersIntegration Testing+18

Ge

Embedded Software Engineer

Mar 2019 – Jan 2021 · 1 yr 10 mos · Hyderabad, Telangana, India

Software DevelopmentObject-Oriented Programming (OOP)Kernel DriversMicrocontrollersIntegration TestingBootloader+15

Cdac

Academic project

Nov 2018 – Jan 2019 · 2 mos

Adaptive Lane Detection using Deep Learning and Communication using CAN Bus Protocol
In our Advanced Driver Assistance Systems(ADAS) a video feed from a data-set is fed to the neural network for lane Detection & steering angle calculation. Deep Learning is implemented using TensorFlow library for training the Neural Network. On successful identification of LANE with an error less than 0.04, a socket connection is established between the processing unit and CAN bus-master Beaglebone Black. Steer angle is passed to the steering control ECU(STM32F4) over the CAN-BUS.

Software DevelopmentObject-Oriented Programming (OOP)Kernel DriversMicrocontrollersDebuggingRust (Programming Language)+10