Abhijeet Bajaj

Abhijeet Bajaj is a skilled software engineer with expertise in machine learning, distributed systems, and large-scale software development. With a Master’s degree in Computer Science from Columbia University (CGPA: 3.83/4.0) and a Bachelor of Engineering in Computer Science from BITS Pilani (CGPA: 8.43/10.0), he possesses a strong academic foundation in algorithms, databases, and AI-driven applications. Currently, he works as a Software Engineer II at Uber, where he has played a crucial role in enhancing the marketplace dynamics through driver surge pricing algorithms. His work has contributed to launching novel pricing models in 15 Latin American cities and building metric pipelines to monitor the health of these systems. Prior to Uber, Abhijeet gained valuable experience as a Software Engineer Intern at Google Cloud Network, where he developed a real-time dashboard to monitor network health and implemented a command-line interface (CLI) that detects and analyzes anomalous events. His work involved utilizing DBSCAN clustering techniques to find correlations in network failures, significantly reducing on-call troubleshooting time. Additionally, he worked as a Strategy Analyst at Goldman Sachs, where he was responsible for reviewing bonds for the Rates & Credits team and monitoring software-based control mechanisms in electronic trading. His experience at Samsung Research as a Research Intern further honed his expertise in computer vision, where he worked on depth-based annotation techniques for RGB-Depth images and improved upon methodologies for real-time human pose estimation. His technical proficiency spans multiple programming languages, including C/C++, Python, and Java, as well as deep learning frameworks such as PyTorch, TensorFlow, and Keras. His coursework in distributed systems, data structures, and object-oriented programming has provided him with the knowledge to solve complex engineering problems. Throughout his career, Abhijeet has demonstrated a strong aptitude for problem-solving and optimization, evident in his projects like a multi-threaded chatbot with multimodal input and an A* search-based pathfinding solution for Google Maps covering 40 square kilometers of New Delhi. His approach to software development integrates AI-driven solutions with practical applications, as seen in his contributions to anomaly detection, trading risk mitigation, and AI-based perception models. He is also a winner of the 2019 Tesco Graduate Hackathon, where he co-developed a novel data lineage extractor that visualized SQL script dependencies through a collapsible tree. His commitment to innovation, along with his ability to manage concurrent processes, optimize data structures, and design scalable AI models, makes him a highly valuable asset in the software engineering landscape. With expertise in REST APIs, Git, JIRA, SQL, and various AI and ML libraries, Abhijeet continues to drive impactful projects that bridge the gap between theoretical advancements and real-world applications.