• Design and implement software for neuro42’s portable MRI devices, from requirements to production and commercial deployment

• Developed and implemented test protocols for MRI subsystems (RF, GPA, SDR, PLC), improving consistency, test coverage, and repeatability

• Collaborated with hardware engineers to investigate PCBA failures, implement corrective actions, and strengthen design testability

• Automated firmware/software deployment and validation pipelines using CI/CD tools like Jenkins and Git to maintain consistent test environments

• Authored weekly metrics decks with visualizations and concise narratives; facilitated cross-functional readouts and decision tracking

• Analyze and enhance the efficiency, stability, and scalability of system resources, ensuring optimal performance of neuro42’s MRI technology

• Design, develop, test, and debug system software, supporting software QA and optimizing I/O performance for accurate and rapid MRI data processing

• Interface with hardware design and development teams to integrate embedded software seamlessly with neuro42’s advanced imaging hardware

• Develop infrastructure for the secure transmission of sensitive clinical data to the cloud, ensuring compliance with healthcare data regulations

(HIPAA)

• Support engineering teams by developing automation and test tools, enhancing the development life-cycle and ensuring the reliability of neuro42’s MRI systems

• Developed test scripts for automation and data analysis to diagnose and troubleshoot failed units with GPS issues from field

• Defined test strategy, detailed test plan/protocol for new Hardware designs or value added engineering projects

• Created test plans based on requirements in order to test the specific hardware modules

• Developed and executed test protocols, document test results based on test purpose and system under test

• Assisted in evaluation of electronic circuits and RF boards

• Conducted failure analysis to troubleshoot failed units from field and factory tests in a lab

• Lead the research and development phases of a cutting-edge chatbot project designed to serve as a teaching assistant for students

• Ingested a diverse range of educational resources into Elastic Search, optimized vector embeddings, and implemented Retrieval Augmented Generation (RAG) technology to enhance the chatbot’s knowledge base. Ensured seamless integration with Elastic Search for efficient content retrieval