Rutul Gandhi

• Designed, implemented, and validated deep learning pipelines for unsupervised segmentation of 4D FMCW LiDAR point clouds, enabling real-time data processing in operational testbeds (Master's Thesis, industry collaboration). • Developed and calibrated multi-sensor fusion tools (LiDAR + camera), achieving precise, low-latency (<20ms). • Supported system validation through internal and external field testing, including realtraffic test drives in diverse, real-world environments.

Led the migration of simulation systems from ROS1 to ROS2, significantly improving reliability and performance for autonomous vehicle platforms. • Structured and curated datasets to enhance the accuracy and efficiency of embedded machine learning models. • Authored comprehensive technical documentation to support team onboarding, process transparency, and knowledge transfer. • Collaborated with engineering teams to streamline workflows and deliver robust, real-time AI solutions for automotive applications.

• Developed end-to-end machine learning algorithms and NLP pipelines (SpaCy, Prodigy NLTK), supporting advanced text analytics and automation for client projects. • Built and optimized REST APIs (FastAPI, Django) and dynamic web modules, ensuring robust integration and scalable application performance. • Managed and tuned relational and NoSQL databases (PostgreSQL, MongoDB, Neo4j), delivering fast and reliable data retrieval for analytics workflows.