Professional Overview
I'm a Deep Learning Engineer focused on building systems where models reason, act, and collaborate. With a foundation in physics and mechanical engineering, I specialize in applying AI especially vision + LLM systems to real-world tasks. I’ve built agentic AI workflows using LangChain, LangGraph, and ReAct, such as Linked Squad (a multi-agent LinkedIn optimizer) and DoqToq (a modular RAG system that gives documents a voice). On the vision side, I work with GANs, LaMa, UNet, Pix2Pix, Diffusers, and Fourier pipelines for inpainting, segmentation, and image generation. I focus on scalable, optimized deployment building pipelines with PyTorch DDP, Docker, and Flask and I enjoy turning research ideas into robust, usable systems. My toolkit includes PyTorch, Hugging Face, LangChain, LangGraph, MLflow, GCP, and more.
Areas of Expertise
computer vision
Develop image recognition, object detection, video analysis, and multimodal AI systems for real-world applications.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
