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Privacy Policy

Job Description

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Key Responsibilities

  • Developed and deployed end-to-end machine learning models using Python, TensorFlow, and Scikit-learn, achieving a 20% improvement in predictive accuracy.
  • Built scalable data pipelines to process structured and unstructured datasets using tools like Apache Spark, Pandas, and SQL, reducing data processing time by 40%.
    Implemented NLP models using Hugging Face Transformers and spaCy to extract insights from customer reviews, improving sentiment analysis accuracy by 25%.
  • Deployed AI models using Docker and Kubernetes in a CI/CD environment, ensuring robust and automated model lifecycle management.
  • Fine-tuned pre-trained deep learning models (e.g., BERT, ResNet) to meet specific business requirements, reducing training time by 30%.
  • Designed and evaluated algorithms for recommendation systems, achieving a 15% increase in user engagement on the platform.
  • Collaborated with cross-functional teams to integrate AI solutions into production, aligning with business KPIs and customer needs.
  • Applied computer vision techniques using OpenCV and YOLO for real-time object detection, reducing false positive rates by 18%.
  • Created automated dashboards and reports for model performance tracking using tools like Tableau and MLflow.
  • Researched and experimented with cutting-edge AI techniques, leading to a proof-of-concept implementation of Generative AI for text summarization.