I’m a Lead Algorithm Developer at Applied Materials, building computer vision and machine learning systems for industrial inspection—where reliability, scale, and measurable business impact matter. I work end-to-end across the applied ML lifecycle: aligning stakeholders on success metrics, delivering production-grade solutions, and continuously improving systems after deployment. My current work includes AI-driven inspection workflows, segmentation-based approaches, and generative modeling (GANs), including synthetic defect generation to expand data coverage and improve robustness. Previously, I contributed to advanced driver assistance perception at Mercedes‑Benz R&D India, developing practical, production-focused solutions across 3D perception (BEV-style representations), freespace understanding, and trajectory prediction using VAE-based generative models (including conditional variants). I also supported data science initiatives involving large-scale vehicle analytics, geo-spatial mining, and statistical insight generation. Earlier, at Volkswagen Group Innovation, I worked on safety-oriented learning for automated driving and proposed a novel loss to improve temporal stability in segmentation networks, supported by systematic experiment tracking. I’m also a PhD Researcher at IIIT Dharwad, exploring LLM + GraphRAG / Knowledge Graph methods for more grounded and trustworthy intelligent systems. I’m an inventor on multiple patents and authored an award-winning paper (Best Paper at a CVPR workshop). Beyond my core work, I enjoy staying close to the latest advances in ML—exploring new ideas to push boundaries and improve outcomes. I’m also committed to mentoring and sharing knowledge, with a focus on building systems that drive innovation and business results.