08/09/2026 updated
100 % available
Senior Embedded Systems Engineer | Embedded C/C++ | FreeRTOS | Edge AI | TinyML | HiL Validation
Bengaluru, India
Only remote
M. Tech in Electrical and Electronics Engineering, Indian Institute of Technology (IIT) GuwahatiAbout me
Senior Embedded Systems Engineer with 4+ years in Automotive & BESS. Skilled in Embedded C/C++, Python/PyTest, HiL, firmware validation, TinyML, TensorFlow Lite Micro, INT8 quantization, and FreeRTOS. Experienced in real-time Edge AI and scalable embedded software.
C++ (Programming Language)DebuggingEmbedded CEmbedded SystemsEmbedded Operating SystemsEmbedded SoftwareFirmwareFreeRTOSHardware-In-The-Loop SimulationIntegrated Electronic ControlsTensorFlowSimulinkPytestTesting (Hardware)Artificial Intelligence Markup Language (AIML)
Embedded C/C++ & RTOS Development
Expertise in Embedded C and C++ programming combined with real-time operating systems such as FreeRTOS and ESP32. Development of firmware for resource-constrained microcontrollers including task prioritization, mutex synchronization, and deterministic scheduling.
Hardware-in-the-Loop (HiL) Validation
Extensive experience architecting and executing HiL validation strategies using platforms such as Typhoon HIL 606 and dSPACE SCALEXIO. Design of end-to-end Verification and Validation frameworks for automotive ECUs and Battery Energy Storage System controllers in compliance with ASPICE SWE.5/SWE.6 and ISO 26262 ASIL B/C standards.
TinyML & Edge AI
Design and deployment of TinyML pipelines using TensorFlow Lite Micro, Quantization-Aware Training (QAT), and INT8 quantization on embedded devices. Development of real-time inference engines in Embedded C++ for edge AI applications including battery anomaly detection and state-of-charge estimation.
Python Test Automation
Development of Python-PyTest automation frameworks for fault injection, hardware-software integration, and RTOS timing verification. Integration of HiL automation into CI/CD pipelines using Allure and TestRail, improving requirement traceability and reducing regression testing time.
Vehicle & Communication Protocols
Proficiency in automotive and industrial communication protocols including CAN, LIN, FlexRay, MQTT, Ethernet, MODBUS, I2C, and UART. Validation of AUTOSAR-based powertrain ECUs using CANoe, INCA, and ECU-to-Cloud interfaces.
MATLAB/Simulink Modelling
Modelling and simulation using MATLAB/Simulink for MiL, SiL, and HiL environments. Experience with FMI/FMU, MBSE, and Typhoon Schematic Editor for system-level design and verification.
AI & Machine Learning
Application of TensorFlow, TensorFlow Lite, and TensorFlow Model Optimization Toolkit (TFMOT) for developing and optimizing neural network models. Use of Scikit-learn for machine learning tasks and development of AI-assisted engineering workflows using Retrieval-Augmented Generation (RAG) and local Large Language Models (LLMs).
Diagnostic Tools & Configuration Management
Hands-on experience with diagnostic tools such as CANoe, INCA (JTAG), TestRail, SCADA, and ProveTech. Configuration management using DOORS, STARC, Git, JIRA, XRAY, and Confluence.
Expertise in Embedded C and C++ programming combined with real-time operating systems such as FreeRTOS and ESP32. Development of firmware for resource-constrained microcontrollers including task prioritization, mutex synchronization, and deterministic scheduling.
Hardware-in-the-Loop (HiL) Validation
Extensive experience architecting and executing HiL validation strategies using platforms such as Typhoon HIL 606 and dSPACE SCALEXIO. Design of end-to-end Verification and Validation frameworks for automotive ECUs and Battery Energy Storage System controllers in compliance with ASPICE SWE.5/SWE.6 and ISO 26262 ASIL B/C standards.
TinyML & Edge AI
Design and deployment of TinyML pipelines using TensorFlow Lite Micro, Quantization-Aware Training (QAT), and INT8 quantization on embedded devices. Development of real-time inference engines in Embedded C++ for edge AI applications including battery anomaly detection and state-of-charge estimation.
Python Test Automation
Development of Python-PyTest automation frameworks for fault injection, hardware-software integration, and RTOS timing verification. Integration of HiL automation into CI/CD pipelines using Allure and TestRail, improving requirement traceability and reducing regression testing time.
Vehicle & Communication Protocols
Proficiency in automotive and industrial communication protocols including CAN, LIN, FlexRay, MQTT, Ethernet, MODBUS, I2C, and UART. Validation of AUTOSAR-based powertrain ECUs using CANoe, INCA, and ECU-to-Cloud interfaces.
MATLAB/Simulink Modelling
Modelling and simulation using MATLAB/Simulink for MiL, SiL, and HiL environments. Experience with FMI/FMU, MBSE, and Typhoon Schematic Editor for system-level design and verification.
AI & Machine Learning
Application of TensorFlow, TensorFlow Lite, and TensorFlow Model Optimization Toolkit (TFMOT) for developing and optimizing neural network models. Use of Scikit-learn for machine learning tasks and development of AI-assisted engineering workflows using Retrieval-Augmented Generation (RAG) and local Large Language Models (LLMs).
Diagnostic Tools & Configuration Management
Hands-on experience with diagnostic tools such as CANoe, INCA (JTAG), TestRail, SCADA, and ProveTech. Configuration management using DOORS, STARC, Git, JIRA, XRAY, and Confluence.
Experience
Professional experience0 - 5 years
Freelancer since2026
Languages
EnglishFluentHindiNative speaker
Project history
Architected a Python-PyTest Hardware-in-the-Loop (HiL) automation framework and end-to-end Verification and Validation strategy for inverter and Battery Energy Storage System (BESS) controllers, reducing regression testing time by 30%. Executed real-time firmware V&V for RTOS and grid-compliance scenarios using Typhoon HIL, SCADA, and TestRail. Integrated HiL automation into CI/CD pipelines, designed reusable PyTest automation frameworks, and developed AI-assisted engineering workflows using RAG and local LLMs.
Built a proof-of-concept demonstrating concurrent execution of two TinyML models on a single-core ESP32. Explored task prioritization, mutex synchronization, and deterministic scheduling using FreeRTOS. Deployed TensorFlow Lite Micro models in Embedded C++ for real-time inference and benchmarked memory footprint and inference performance on a resource-constrained MCU. Technologies: Embedded C++, Python, FreeRTOS, ESP32, TensorFlow Lite Micro, INT8 Quantization, DSP.
Developed an end-to-end TinyML pipeline using Quantization-Aware Training (QAT) to deploy an INT8 autoencoder for real-time battery anomaly detection on resource-constrained embedded devices, achieving more than 98% model compression (343 KB to 4 KB). Engineered a bare-metal C++ inference engine with DSP-based signal filtering for deterministic, low-latency offline anomaly detection, generating deployable TensorFlow Lite, C++ header, and Intel HEX artifacts for embedded firmware integration.
Certificates
ISTQB
ITB2024