Mohammad Aniq

Machine Learning Engineer and Computer Science undergraduate specializing in end-to-end AI/ML systems across Deep Learning, Generative AI, and MLOps. I focus on taking models from experimentation to scalable, production-ready deployment with strong emphasis on monitoring, reliability, and continuous improvement.

I’ve built LLM-powered applications, RAG pipelines, and ML observability systems, including a platform for real-time drift detection and an end-to-end predictive analytics system with automated retraining and CI/CD.

Skilled in Python, PyTorch, TensorFlow, FastAPI, Docker, AWS, LangChain, and MLflow, with a focus on scalable pipelines, experiment tracking, and reproducibility.

Engaged in an AI bootcamp, building expertise in machine learning, deep learning, and data analytics. Developed AI-powered applications, including an AI pilot assistant and an AI sales agent, utilizing Grok, LangChain, Hugging Face, and LLMs. Hands-on experience in ML model development, training, and deployment using Google Colab, Kaggle, and Jupyter Notebook.

Tools and Technologies

Machine Learning & AI – Grok, LangChain, Hugging Face, LLMs, Scikit-learn (sklearn), ML Algorithms

Programming Languages – Python, SQL

Data Analytics – Pandas, NumPy, Matplotlib, seaborn

Cloud & Deployment – AWS, Google Cloud

Development Frameworks – PyTorch, TensorFlow

Model Training & Experimentation – Google Colab, Kaggle, Jupyter Notebook

Deployment & UI – Gradio, Streamlit