Ritesh

Pune, Pune Division, Maharashtra, India
Ritesh
Experience: 1 years
Open to: Full-Time
Education: Bachelors
Availability: Within 30 Days
Skills: CPP, Jupyter Notebooks, LLMs/ChatGPT, pandas, PowerShell, Python, Python3, PyTorch, TensorFlow
Previously worked at: Startups
Assessment Score: 75
  • Professional Experience
    A Machine Learning Engineer with hands-on experience in deep learning, computer vision, and NLP. Currently working at SmartQR Technologies (HealthLedger Diagnostics), where I lead the development of advanced models like YOLOv8 and EasyOCR to enhance diagnostic accuracy and health-related applications.
  • Technical Expertise
    Proficient in programming languages such as C, C++, Python, and SQL, with expertise in libraries and frameworks like TensorFlow, PyTorch, Langchain, Yolo, and OpenAI. Strong background in machine learning, deep learning, generative AI, and computer vision technologies.
  • Academic Background
    Currently pursuing a B-tech in Computer Science Engineering from JSPM’s Rajarshi Shahu College of Engineering, Pune, with a CGPA of 8.83. Completed SSC and HSC with high distinctions, demonstrating a solid academic foundation.
  • Key Projects
    Developed a Real-Time Body Augmentation system using the Barracuda model and Unity, and created a Mobile Price Prediction system with 95% accuracy. Also, developed web scraping solutions using BeautifulSoup, Langchain, and OpenAI for advanced data extraction and question-answering tasks.
  • Certifications and Training
    Completed certifications in Machine Learning, Python, C/C++, and Generative AI from prestigious platforms including Google Cloud and Cisco Networking Academy. Demonstrated practical knowledge through project-based learning and professional development.
  • Achievements and Recognition
    Secured second place in a project competition organized by ACM, and won multiple sports awards such as a cricket tournament and table tennis competition. Actively involved in R&D, improving product accuracy with cutting-edge models like YOLOv9 and YOLO-NAS.

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