Wajahat H. is a highly skilled and versatile Machine Learning Engineer with a BS in Computer Science from the National University of Sciences and Technology (NUST). Wajahat demonstrates expertise across a wide range of modern technologies including Python, C++, Java, JavaScript, scikit-learn, matplotlib, spaCy, Transformers, ViT, YOLO, TensorFlow, Keras, LangGraph, SQL, PostgreSQL, Oracle, SQL Server, Airflow, Selenium, DuckDB, FastAPI, Django, Git, Docker, pgvector, Redis, S3, Flask, Playwright, and Streamlit. His experience spans computer vision, natural language processing, transfer learning, data engineering, retrieval augmented generation (RAG), generative AI with large language models, medical imaging, web scraping with Selenium, REST API development, and end-to-end data pipelines. Wajahat has held impactful engineering roles at Red Buffer and Data Quartz, where he architected incident management systems, multi-agent orchestration workflows, real-time operational tools, and microservices, as well as developed innovative projects in fracture detection on X-rays, skin tone and pimple classification, and sentiment-based customer service automation using technologies like YOLOv8, LSTM, gladia, talkscriber, whisper, Prefect, Airflow, JDBC, OpenAI, OAuth 2.0, JWT, and HTML/CSS/JS. With proven soft skills in leadership and team collaboration, as well as strong assessment scores in project and problem-solving topics, Wajahat brings a data-driven, solution-oriented approach and a passion for pushing the boundaries of applied machine learning and AI systems.