Innovative AI Engineer and Data Scientist with a strong foundation in machine learning, deep learning, NLP, and large
language model (LLM) integration. Demonstrated ability to architect and ship production-ready AI syst…
Innovative AI Engineer and Data Scientist with a strong foundation in machine learning, deep learning, NLP, and large
language model (LLM) integration. Demonstrated ability to architect and ship production-ready AI systems — from
computer vision pipelines and generative AI applications to real-time data platforms — delivering measurable business
impact across each engagement. Proficient in Python, TensorFlow, PyTorch, Hugging Face, and LangChain, with hands-on
experience in cloud-based AI workflows (Microsoft Azure) and MLOps practices. Backed by 7+ end-to-end project deliveries,
2 industry internships, and 10+ technical certifications, I bring both the engineering depth and collaborative mindset that
high-performing AI teams demand. Eager to solve complex, real-world problems and drive intelligent product innovation
from day one.
Engineered 4 cross-platform mobile screens in Flutter using reusable widget architecture, reducing estimated development time by ~35% versus building from scratch.
Built PHP backend modules for form handling and dynamic rendering, integrating with MySQL to support persistent data storage for 500+ records.
Maintained clean Git commit history throughout the project, enabling seamless code reviews and zero merge conflicts across the entire internship duration.
Engineered 4 cross-platform mobile screens in Flutter using reusable widget architecture, reducing estimated development time by ~35% versus building from scratch., Built PHP backend modules for form handling and dynamic rendering, integrating with MySQL to support persistent data storage for 500+ records., Maintained clean Git commit history throughout the project, enabling seamless code reviews and zero merge conflicts across the entire internship duration.
Processed and analyzed 3+ real-world security datasets using Python, Pandas, and NumPy; reduced data inconsistencies by ~40% through structured preprocessing pipelines.
Built EDA workflows using Matplotlib and Seaborn, surfacing key patterns that directly informed anomaly detection strategy decisions for the engineering team.
Developed and deployed a supervised classification model on live security data achieving ~82% accuracy, demonstrating practical machine learning application in a production business context.
Processed and analyzed 3+ real-world security datasets using Python, Pandas, and NumPy; reduced data inconsistencies by ~40% through structured preprocessing pipelines., Built EDA workflows using Matplotlib and Seaborn, surfacing key patterns that directly informed anomaly detection strategy decisions for the engineering team., Developed and deployed a supervised classification model on live security data achieving ~82% accuracy, demonstrating practical machine learning application in a production business context.
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