Shiva Pitla
Computer Science graduate with strong hands-on experience in Python, Machine Learning, Deep Learning, and Generative AI, gained through building and d...
About
Computer Science graduate with strong hands-on experience in Python, Machine Learning, Deep Learning, and Generative AI, gained through building and deploying end-to-end AI applications. Skilled in classical ML, deep learning, and LLM-powered systems, with a track record of rigorous model evaluation and real-world deployment.
Skills
Core CS Concepts
Object-Oriented Programming (OOP)
Database Management Systems (DBMS)
Machine Learning
Machine Learning
ML Algorithms
Random Forest
Classification
Regression
Model Evaluation (Accuracy, Precision, Recall, F1, R2)
Scikit-learn
Deep Learning
Deep Learning
NLP
Computer Vision
TensorFlow
Keras
PyTorch (Fundamentals)
LLMs & Generative AI
Large Language Models (LLMs)
Prompt Engineering
Retrieval-Augmented Generation (RAG)
LangChain
LLM APIs
OpenAI (GPT)
Groq LLaMA 3.3-70B
HuggingFace
Vector Databases
FAISS
ChromaDB
Pinecone
Web & API Development
REST APIs
FastAPI
Streamlit
Version Control & Tools
Git
GitHub
Debugging
Libraries
Pandas
NumPy
Matplotlib
Projects
AI PDF Chatbot using RAG
Python
LangChain
FAISS
HuggingFace
Groq LLaMA 3.3-70B
Streamlit
REST API
Designed an intelligent document Q&A system using Retrieval-Augmented Generation (RAG): PDF ingestion, chunking, embedding, and retrieval via FAISS and HuggingFace. Integrated the Groq LLaMA 3.3-70B model via REST API for context-aware responses; benchmarked chunking and retrieval configurations to improve accuracy.
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Crop Field AI - Crop Recommendation & Yield Prediction
Python
Scikit-learn
Random Forest
Pandas
Streamlit
Built an end-to-end ML application recommending crops from soil/environmental parameters and predicting agricultural yield. Trained a Random Forest Classifier (99.32% accuracy) for crop recommendation and a Random Forest Regressor (R2 = 0.9790) for yield prediction. Serialized models and preprocessing pipelines for consistent inference; deployed the application on Streamlit Community Cloud.
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Movie Recommendation System
Python, NLP, Scikit-learn, FastAPI, Streamlit
Built a content-based recommendation engine leveraging NLP, TF-IDF vectorization, and cosine similarity for personalized movie recommendations.
Performed data cleaning and exploratory data analysis (EDA) on the movie dataset using Pandas and NumPy prior to feature extraction.
Developed REST APIs using FastAPI with an interactive Streamlit frontend; integrated the TMDB API for live movie metadata and posters.
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Experience
MERN Stack Intern
EY Global Delivery Services & AICTE
10/02/2025 - 21/03/2025
Developed a full-stack Food Delivery application using MongoDB, Express.js, React.js, and Node.js as part of a structured internship program. Contributed to JWT-based authentication, shopping cart, and order management features under guided project structure. Collaborated in an agile team environment using Git version control and participated in code reviews.
Education
B.Tech in Computer Science and Engineering
Gurunanak Institute of Technical Campus, Hyderabad
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Certifications
Building Modern Web Applications with MERN Stack
EY Global Delivery Services & AICTE
View Credential →