Kothapalli Nithish
Machine Learning Engineer @Goldman Sachs Group Limited
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WORK HISTORY
Machine Learning Engineer @Goldman Sachs Group Limited
NJ, US
Applied NLP algorithms, including Named Entity Recognition (NER) and Sentiment Analysis using VADER, improving text analysis efficiency by 70% and facilitating insight extraction from social media data.• Deployed a custom BERT-based sentiment analysis model, achieving an F1 score of 0.85 for aspect-based sentiment analysis, providing deep insights into customer feedback and enabling data-driven business decisions.• Collaborated on developing a customer service chatbot using Large Language Models (LLMs), integrating with Confluence to retrieve and parse knowledge base articles, enhancing customer satisfaction by 25%. • Leveraged AWS SageMaker for managing infrastructure and automating workflows, increasing project scalability by 40% through elastic resource provisioning, and enhancing agility with faster model training, and deployment.• Enhanced monitoring and operational excellence of ML systems by leveraging AWS CloudWatch to identify and address issues, resulting in a 30% decrease in issue resolution times and a 50% uptime for critical ML workflows.• Employed Latent Dirichlet Allocation (LDA) for topic modeling, uncovering key themes and trends from textual data, with stemming and tokenization preprocessing techniques, and produced interactive visualizations with Plotly.
EDUCATION
New England College
Master's degree
ABOUT KOTHAPALLI NITHISH
Machine Learning Engineer with 4+ years of experience in Artificial Intelligence, Deep Learning, Machine Learning, Data Mining, Data Visualization, and Natural Language Processing (NLP). • Excellent in Python to manipulate data for data loading and extraction and worked with Python and R libraries like NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, Seaborn, TensorFlow, Ggplot2, OpenCV, PyTorch and NLTK. • Expertise in building machine learning models using algorithms such as Linear Regression, Logistic Regression, Support Vector Machines, Decision trees, KNN, K-means Clustering, and Ensemble methods (AdaBoost, Bagging, Gradient Boosting). • Proficient in implementing and fine-tuning deep learning algorithms, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTM, Transformers, BERT, GPT, and Llama
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