I am a Machine Learning Engineer and Computer Engineering scholar with hands-on experience building deep learning models, automated data pipelines, and production-oriented AI applications. My work spans machine learning, data science, LLM alignment, software engineering, and AI training workflows.
I specialize in Python-based machine learning development using TensorFlow, scikit-learn, Pandas, and NumPy. I have built classification systems, forecasting engines, ensemble models, feature engineering pipelines, and real-time anomaly detection solutions.
I have experience with AI data training, technical data annotation, prompt engineering, prompt evaluation, and Reinforcement Learning from Human Feedback (RLHF). I focus on evaluating model quality, reducing hallucinations, enforcing domain boundaries, and supporting technically safe LLM outputs.
I have engineered end-to-end data preprocessing workflows for medical image datasets and financial time-series data. My projects include skin cancer classification with EfficientNet, network intrusion detection, quantitative market intelligence, and an LLM-powered solar energy advisory system.
Beyond machine learning, I develop backend and full-stack systems using Flask, REST APIs, React Native, Firebase, and JavaScript. I also have foundational networking and security expertise covering Cisco routing, VLANs, packet inspection, secure code auditing, and penetration testing fundamentals.
I am a continuous learner with certifications in reinforcement learning, data science, networking, and ICT security. I aim to contribute to AI, machine learning, data science, and software engineering teams where I can build reliable, measurable, and impactful intelligent systems.
