I am an aspiring AI Engineer with a strong foundation in Artificial Intelligence, Machine Learning, and Deep Learning. I am skilled in implementing and fine-tuning models like GPT and BERT using Python and open-source libraries. My experience includes optimizing model performance with techniques such as quantization and Mixture of Experts. I am proficient in developing AI applications, including NLP pipelines and classification systems, and I aim to leverage my expertise to build scalable, production-ready solutions.
- 🔭 I’ve worked at Teamlift.co
- 🌱 I’m currently learning Agentic AI, MLOPs, Observability, Scalability
- 👨💻 All of my projects are available at yasirrazaa.github.io
- 💬 Ask me about Python, Machine Learning, Deep Learning, LLMs, and MLOps, Agentic AI
- 📫 How to reach me: yasirabdali6@gmail.com
- 📄 Know about my experiences: yasirrazaa.github.io
- Python
- SQL
- Docker
- Git
- GitHub Actions
- Linux
- Bash
- pandas
- NumPy
- PyTorch
- TensorFlow
- Unsloth
- Transformers
- FastAPI
- Streamlit
- Scikit-learn
- Plotly
- Langchain
- LlamaIndex
- MySQL
- PostgreSQL
- Urdu (Native)
- English (Fluent)
- Collected data from diverse sources using web scraping and API integration.
- Preprocessed and transformed messy web data for further analysis.
- Applied Named Entity Recognition (NER) to extract skills from job descriptions.
- Prepared data for time series forecasting and built models to predict future values.
- Collaborated with data scientists and engineers to design and implement data-driven solutions.
- Built end-to-end pipeline to collect, process, and annotate large amounts of data.
Bachelor's Degree, Software Engineering at National University of Modern Languages, Islamabad, Pakistan (Sep 2022 - Jul 2026)
- Coursework: Statistics & Probability, Artificial Intelligence, Databases
- Generated synthetic data using LLMs and validated generated data using Sympy.
- Fine-tuned Numiba-math-7B.
- Built and published an MCP server on PyPi to efficiently work with Jupyter Notebooks when using AI for coding.
- Deployed the SOTA voice agent on Runpod as a serverless endpoint.
- Built a chatbot to chat with documents.
- Implemented a real-time emotion detection using CNN, OpenCV, Streamlit, and WebRTC.
- Built a web app to analyze group and individual chats from a text file.
- Helped a researcher achieve SOTA results on WLASL dataset.
- Deployed the SOTA voice model ChatterBox that beats Eleven Labs on Runpod as a serverless endpoint.
- Advanced LLM Bootcamp: NUST & Sky Electric
- Generative AI Fundamentals, Generative AI, Responsible AI, Intro to Large Language Models: Google
- Data Scientist Track: DataCamp
- Python: Kaggle
