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About me



  • 2019 - 2022

    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.

  • 2018 - 2019
    | Tvasthaa Data Solutions

    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.