I am an AI and software engineer with 7+ years of experience building practical systems that transform complex operations into clear, reliable user experiences.

• Working on: intelligent dashboard systems and real-time decision support.
• Focus areas: deep learning, NLP, reinforcement learning, machine learning, computer vision, data mining, AI programming, and web development.
• Goal: ship robust, measurable products that improve safety and operational efficiency.
• Open to: collaboration on AI, automation, data-centric product engineering, and building scalable intelligent systems.

• Programming: Python, C++, JavaScript, Java
• AI & ML: TensorFlow, PyTorch, Scikit-learn, NLP
• Web Development: Node.js, Express, React, HTML, CSS
• OpenAI API
• ***REST API, FASTAPI
• Tools: Git, Linux, Docker, VS Code
• Design and develop AI-assisted applications from concept to deployment.
• Build clean, maintainable Python and C++ backends and data workflows.
• Create dashboards that make mission-critical information easy to act on.
• Translate business and operations needs into scalable technical systems.

Customer segmentation using K-means clustering
📊 Data mining + machine learning

Creative web design project
🎨 UI/UX + frontend development

• RAG-PDF-PROJECT: the interface applications of RAG-PDF-PROJECT (click the link to running in huggingface)
• semantic-RAG: the interface applications of semantic-RAG (click the link to running in huggingface)
• Churn Risk Prediction: Predicts customer churn risk from tabular data and tunes decision thresholds to improve precision-recall tradeoffs.
• Catch Game with DQN: Implements a Deep Q-Network AGENT for a game environment using reward-driven policy learning.
• Mountain Car DQN: Reinforcement learning project that trains an AGENT to solve the MountainCar task with value-based methods.
• UMAP Clustering: Demonstrates dimensionality reduction and clustering workflows for exploratory data analysis.
• XGBoost Classification: Builds classification pipelines with gradient boosting for practical supervised learning problems.
• Working in a Railway obstacle-detection startup.
• Addressing blind spots in the heavy transport industry.
• Building AI models for wildfire-related applications.

• SENTIMENT-NLP-: End-to-end sentiment analysis project with DistilBERT, training pipeline, and Streamlit app.
• Pytorch: Neural network learning notes and PyTorch implementation exercises.
• Mountain-car-DQN-: Reinforcement learning project using Deep Q-Network on MountainCar.
• -Catch-Game-with-DQN: DQN agent for the Catch game with reward-driven policy learning.

• Building production-ready AI features with clear ROI
• Improving reliability and observability in deployed systems
• Publishing cleaner documentation and technical writeups

• Email: ebimahmudali@gmail.com
• Linkedin : realebimhdl@gmail.com

If you find my work useful, feel free to star a repository or reach out for collaboration.