I engineer production-ready AI systems that bridge cutting-edge research with scalable software development. Specializing in Deep Learning, Computer Vision, NLP, and AI-driven software engineering, I design intelligent solutions that are optimized for real-world deployment, performance, and maintainability.
My expertise includes building advanced CNNs for image analysis, YOLO-based object detection and segmentation pipelines, transformer-based NLP systems, and multimodal AI architectures that combine vision and language understanding. I also develop Edge-AI applications on platforms like Raspberry Pi, enabling low-latency inference and real-time decision-making in constrained environments.
Beyond model development, I focus heavily on the Software Development Engineering (SDE) lifecycle — including API development, CI/CD pipelines, Docker & Kubernetes deployment, cloud integration, monitoring, scalable backend architectures, and MLOps workflows. I work with technologies such as PyTorch, TensorFlow, FastAPI, GitHub Actions, MLflow, and modern DevOps stacks to ensure seamless AI deployment.
What differentiates me is my ability to transform complex datasets, research-level concepts, and experimental models into efficient, deployable AI products that deliver measurable impact with high accuracy, scalability, and production reliability.