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About me



  • 2023 - 2023
    University at Buffalo (SUNY), Buffalo, New York

    Research Study Assistant

    • Conducted comprehensive research on automating the robot to solve Rubik’s race puzzle, gaining expertise in the
    dynamics of the AI Vision arm robot and the intricacies of camera calibration and tile recognition.
    • Implemented AI search algorithms, including IDA* and A*, integrated with pattern databases, resulting in remarkable
    accuracy ranging from 90-100%, improving the robot\’s ability to solve the puzzle.
    • Gained proficiency in MLOps practices & scalable ETL pipelines, which included model development, deployment,
    and optimization, facilitating the seamless deployment of AI models to edge devices.
    • Led efforts to explore innovative solutions through the application of reinforcement learning algorithms, including
    PPO, Agent 57, and Actor-Critic techniques.

  • 2019 - 2022
    Larsen & Toubro Infotech Ltd. (LTI)

    Machine Learning Engineer

    • Implemented MLOps practices with MLflow and Docker for model tracking and container orchestration, leading to a
    20% reduction in operational costs and a 15% improvement in model reliability.
    • Proficiency in handling imbalanced data, and feature scaling; improved scalability and performance of the model by
    improving pre-processing and inference times resulting in an 83.2% smaller model.
    • Enhanced ML models with curated datasets and crafted efficient REST APIs using FastAPI. Integrated deep learning
    models, cutting response time by 25%, elevating user satisfaction on the web app.
    • Developed an ensemble ML model with NLP and time-series forecasting to analyze employee productivity. Processed
    logs and timelines, yielding a 15% improvement in project efficiency.
    • Adept at translating technical concepts for diverse teams and streamlined communication led to successful integration
    of ML solutions in three cross-functional projects, enhancing inter-team collaboration.
    • Designed and deployed end-to-end machine learning pipelines, utilizing TensorFlow and MLOps pipeline, achieving
    scalable and maintainable production solutions.