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Devoprasad Sunil Nedungade

Devoprasad Sunil Nedungade

AI specialist | Machine Learning Engineer

Technology / Internet

London, Greater London

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About Devoprasad Sunil Nedungade:

Motivated MSc Artificial Intelligence graduate with a strong foundation in data science, machine learning, and advanced analytics. Proven experience in deploying AI solutions for predictive maintenance, decision support applications, and performance optimization. Skilled in Python programming, mathematical modeling, and cross-functional collaboration.

Experience

AI Trainer - Outlier AI (Remote) | November 2024 – Present

  • Trained multilingual AI systems and large language models (LLMs) with global teams.
  • Managed coding tasks to improve AI model efficiency and accuracy.
  • Enhanced deliverable quality using advanced AI tools and platforms.

Volunteer Research and Client Support - WP COMMS LTD (London) | July 2024 – August 2024

  • Conducted market research and data analysis to support strategic initiatives.
  • Improved client decision-making efficiency by 20%.
  • Collaborated with clients and prepared impactful presentations.

AI Intern - Aluzie Ltd (Surrey, UK) | April 2023 – June 2023

  • Designed generative AI solutions, saving 25% in project time.
  • Improved project evaluation efficiency by 15% through comprehensive analysis.
  • Delivered innovative prototype demonstrations, earning recognition for clarity and innovation.

Education

MSc in Artificial Intelligence

University of Surrey | February 2024

  • Grade: Merit
  • Dissertation: Intrusion Detection System using AI/ML Algorithms for Privacy Attacks in Vehicular Networks. Achieved 92% accuracy by leveraging dimensionality reduction techniques.
  • Key Modules: Machine Learning, AI and Law, Computer Vision, Augmented Reality, Innovation Management.

B.Tech in Computer Science and Engineering

Vellore Institute of Technology (VIT), Vellore | 2022

  • Grade: 7.57 CGPA (UK 2:1 equivalent).
  • Final Year Project: Cryptocurrency Price Prediction using Machine Learning and Sentiment Analysis. Improved prediction accuracy by 18% through financial sentiment integration.
  • Key Modules: Applied Linear Algebra, Statistics, Internet of Things, Parallel Computing.

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