Mr Ehtsham

AI Engineer

AI Medical Technology • Full-time

Jan 2024 - Present • 2 yrs 7 mos

As an AI Engineer, I design and develop intelligent solutions using Machine Learning, Deep Learning, Generative AI, and LLM technologies. I help businesses turn complex problems into practical, scalable AI products. Key achievements & responsibilities: • Built AI-powered applications using Python, FastAPI, Django, and modern AI frameworks. • Developed RAG-based applications that allow users to interact with their documents and business data using natural language. • Built and integrated LLM-powered applications using OpenAI, Claude, Gemini, and open-source models. • Developed AI agents and workflow automation using LangChain, LangGraph, and MCP. • Implemented NLP, text classification, embeddings, semantic search, and vector databases. • Developed computer vision solutions using YOLO and deep learning models. • Designed and deployed production-ready APIs and AI backends using FastAPI and Docker. • Integrated databases and cloud services including PostgreSQL, MySQL, MongoDB, AWS, and Azure. • Focused on building reliable, scalable, and business-oriented AI solutions rather than experimental prototypes.

AI & Backend Engineer

Life Insurance Attorney • Self-employed

Dec 2022 - Jan 2024 • 1 yr 1 mo

AI & Backend Engineer Developed and deployed AI-powered backend systems and scalable APIs for web and business applications. Built intelligent solutions using Python, FastAPI, Django, Machine Learning, Generative AI, and LLM technologies. Key achievements: • Developed scalable REST APIs using FastAPI and Django. • Built AI-powered applications using OpenAI, Gemini, Claude, and open-source LLMs. • Implemented RAG pipelines with embeddings, vector databases, and semantic search. • Developed AI agents and automated workflows using LangChain and LangGraph. • Designed backend services integrating PostgreSQL, MySQL, MongoDB, and Redis. • Built machine learning and NLP solutions for classification, prediction, and document processing. • Containerized and deployed applications using Docker and cloud platforms. • Improved backend performance, API reliability, and data-processing workflows for production applications.