Machine Learning Models that Deliver Results
I am a Machine Learning Engineer with a strong focus on building practical and scalable AI solutions. Currently pursuing Computer Engineering at IITRAM, I have developed hands on expertise in machine learning, deep learning, and data driven systems across domains such as healthcare, finance, and computer vision.
I have worked on end to end model development, from data preprocessing and feature engineering to training, evaluation, and deployment. My projects include an AI based mental health detection system, a real time facial recognition attendance system, and a trading model that analyzes market patterns and generates automated insights. I have also contributed to research at IIT Delhi involving EEG and ECG signal analysis for sleep pattern detection, with my work under review in an IEEE journal.
I specialize in Python, TensorFlow, PyTorch, and Scikit Learn, and I focus on building solutions that are accurate, efficient, and ready for real world use. I value clear communication, structured problem solving, and delivering results that align with client goals.
I aim to work with clients who are looking for reliable AI solutions that go beyond experimentation and create meaningful impact.
Work Terms
I follow a structured and transparent approach to ensure smooth collaboration and high quality results.
Before starting, I clearly understand the project requirements, objectives, and expected outcomes. A detailed discussion helps define scope, timelines, and deliverables to avoid ambiguity.
I maintain regular communication throughout the project and provide updates at key stages. Feedback is incorporated iteratively to ensure the solution aligns with expectations.
All work is delivered with clean, well documented code and, where applicable, deployment support or integration guidance. I focus on building solutions that are reliable, scalable, and ready for practical use.
Timelines are mutually agreed upon and strictly followed. For larger or complex projects, milestones can be defined to track progress effectively.
Revisions are supported within the agreed scope. Any additional requirements beyond the initial scope can be discussed and adjusted accordingly.
Payments are expected as per platform terms, with milestone based payments preferred for medium to large projects.
Confidentiality and data security are maintained at all times. Any shared data, code, or project details are handled with complete professionalism.