I’m an ML Engineer specializing in Agentic AI Systems, Token Optimization, and Local LLM Deployment. I help startups and businesses build scalable AI systems while reducing inference costs, token usage, and latency. Many AI applications waste significant money due to inefficient prompts, oversized context windows, unnecessary agent loops, and poor model routing. My focus is on optimizing these systems for production-scale efficiency. What I can help with: • Agentic AI systems using LangChain and LangGraph • Token engineering and prompt optimization • AI cost reduction and inference optimization • Local LLM deployment using Ollama, vLLM, llama.cpp • RAG pipeline optimization • Multi-agent workflows and orchestration • GPU inference optimization and quantization • AI automation systems • Custom AI chatbot development • Python backend systems for AI applications I also have experience in: • Deep Learning and Computer Vision • NLP and LLM applications • Trading automation systems • AI-powered workflow automation • API integrations and backend engineering My goal is not just to build AI systems, but to make them production-ready, scalable, cost-efficient, and reliable. Tech Stack: Python, PyTorch, TensorFlow, LangChain, LangGraph, FastAPI, Docker, Ollama, vLLM, Hugging Face, OpenAI API, Vector Databases, REST APIs, CUDA basics, Linux If you are building AI products and need help optimizing performance, reducing AI costs, or deploying local LLM infrastructure, feel free to reach out.