Summary
Naresh Gurulingan is an AI software engineer with 9 years of experience and six years focused on applied AI, deploying models across robotics, sports analytics, and HD mapping with strict accuracy and latency constraints. He has moved foundation models into production—using Segment Anything and fine-tuning strategies to rapidly boost IoU with minimal labeled data—and built scalable Databricks/Azure pipelines with CI/CD and observability. His work spans generative AI (LLMs and diffusion), multi-task and multi-modal networks, and real-time optimizations using ONNX/TensorRT, reflecting both research rigor (ICML publication) and production impact. Comfortable from low-level runtime tuning to architecting feature stores and per-H3-tile workflows, he blends hands-on engineering with explainability and robustness analyses. Based in Eindhoven, he currently focuses on agentic and document-processing GenAI for transport systems, and often reduces data-prep bottlenecks through region-based pipelines and metric-driven monitoring.
9 years of coding experience
8 years of employment as a software developer
Master of Science (M. Sc.) Autonomous Systems, Master of Science (M. Sc.) Autonomous Systems at Bonn-Rhein-Sieg University of Applied Sciences
Bachelor’s Degree Electronics and Communication Engineering, Bachelor’s Degree Electronics and Communication Engineering at Kumaraguru College of Technology
English, badaga, Tamil, German