cv

Basics

Name Rohith Perumandla
Email rohith.perumandla.12@gmail.com
Phone +17739432531
LinkedIn www.linkedin.com/in/perumandla-rohith/
GitHub www.github.com/Rohith1-p
Summary Graduate Research Assistant with expertise in designing and fine-tuning LLM-based conversational agents, implementing machine learning algorithms, and developing scalable systems on GCP. Experienced Data Scientist skilled in building end-to-end data science systems, optimizing data pipelines, and performing advanced data preprocessing. Proficient in Python, PyTorch, and NLP models.

Experience

  • Sep 2023 - Present
    DePaul University, Graduate Research Assistant | AI Engineer
    • Designed and fine-tuned LLM-based conversational agents (Llama3, Phi-3) using PyTorch to assess cognitive decline in dementia patients, contributing to healthcare advancements.
    • Implemented machine learning algorithms for biomarker extraction from multimodal inputs (speech, text, audio) to enhance diagnostic accuracy.
    • Developed and deployed a scalable system on Google Cloud Platform (GCP) using Nginx, Docker, and WebSockets, enabling real-world testing, serving crucial Medicine applications.
    • Optimized system architecture, reducing response time by 75%, significantly enhancing real-time patient engagement and interaction.
    • Reduced GPU computational costs by 60% through model optimization, efficient inference strategies, and resource allocation improvements.
    • Engineered linguistic biomarker extraction modules, targeting six key impairments in dementia patients.
  • Jan 2022 - Jul 2023
    SetuServ, Data Scientist – Customer Management
    • Built and deployed an end‑to‑end scalable data science and ML system for extracting product reviews, ratings, and product details from e‑commerce websites.
    • Designed and automated MLOps data pipelines, reducing data processing time by 50% and optimizing relational database storage.
    • Performed advanced data preprocessing (cleaning, stopword removal, symbol elimination), EDA, and feature engineering, leading to a 15% improvement in sentiment classification accuracy.
    • Accelerated pilot project delivery by 70% using NLP and deep‑learning models (BERT, NER, zero‑shot classification, Word2Vec) from Hugging Face and spaCy.
    • Built and managed MLOps infrastructure, including a core ML server REST API in Python/Django, processing over half a million data points per day for enterprise‑scale solutions.
    • Improved data pipeline and quality checks by integrating Google Sheets with backend servers using Python, Google Apps Script, Django, scikit‑learn, and PyTorch.

Skills

  • Database & Programming: OOP, Python, R, Java, MATLAB, Data Structures, SQLite, MongoDB, HTML, CSS, JavaScript, Google Apps Script, Arduino
  • Machine Learning Frameworks: NumPy, Pandas, PyTorch, Keras, TensorFlow, Matplotlib, Seaborn, Supervised Learning, Unsupervised Learning, Deep Learning, Clustering, RAGs, Agentic AI, Conda
  • Cloud & Software Tools: AWS, GCP, Django, Git, Kafka, Docker, Databricks
  • AI Tools: Prompt Engineering, LangChain Framework
  • Analytics Tools: Excel, Tableau

Licenses & Certifications

Projects

  • AI Chatbot for Healthcare
    Developed an AI Chatbot using LLM's to provide personalized healthcare assistance and improve patient interactions.
  • Bank Loan Case Prediction
    Developed a predictive model using historical bank loan datasets to assess loan repayment likelihood; leveraged machine learning algorithms including Logistic Regression, SVM, Random Forest, Decision Tree, and K-Nearest Neighbors. Optimized model performance, achieving a 93% accuracy rate, significantly enhancing data-driven lending decisions.
  • Brain Tumor Classification and Segmentation using Deep Learning
    Implemented deep learning models utilizing ResNet50 and convolutional neural network (CNN) architectures to accurately classify and segment brain tumors from medical images, achieving a segmentation score of 0.87