Master’s in Internet of Things,

• Coursework: Deep Learning, Big Data Analytics, Analysis of Algorithms, Pattern Recognition.

Bachelors in Electronics

Machine Learning Engineer

• Effectively contributed to three distinct product lines by creating resilient backend frameworks and integrating machine learning
solutions to address tasks such as sentence matching, document parsing, and data mapping.
• Created a robust Named Entity Recognition model using Spacy and Scikit-learn to accurately identify patient names,
personal identifiers, and drug information from clinical trial documents such as Protocols and CSR reports.
• Developed a framework to detect similarities among sentences within multiple documents. By leveraging the capabilities of
natural language processing models like Transformers, fine-tuned on clinical data, achieved an impressive 75% accuracy.
• Created an interactive data visualization dashboard with R-Shiny and Angular for user-friendly data exploration, leveraging
PyTorch-based machine learning models for clinical data insights resulting in a 40% increase in user interaction.
• Streamlined deployment processes by utilizing Docker for containerization and Kubernetes for orchestrating and scaling.
Managed CI/CD pipelines, ensuring continuous integration and deployment. Saved 20+ hours per week in manual
deployment tasks.
• Developed and integrated machine learning models into real-world products by constructing backend APIs using Flask,
resulting in a seamless user experience and improved product performance. Reduced customer support issues by 40%.
• Wrote comprehensive test cases using pytest, adhered to best coding practices, and ensured thorough documentation to
facilitate efficient knowledge transfer within the team, enhancing code reliability and understandability.

Machine Learning Engineer

• Built a text similarity model using Scikit-learn and NLTK, utilizing word embeddings to map columns across different
datasets, resulting in a 30% reduction in data mapping errors.
• Built a Django backend for a document editor app, seamlessly integrating MongoDB for version tracking and Amazon S3
for robust file storage, ensuring efficient data management.
• Played a key role in MLOps by implementing Amazon SageMaker pipelines for deploying text similarity and document
processing ML models. Achieved a 40% reduction in production workflow deployment time.