Hi, I'm Katta Sai Pranav Reddy
Building scalable, production-grade machine learning pipelines, Retrieval-Augmented Generation (RAG) engines, and automated MLOps architectures. Proven track record in high-recall diagnostic models, sub-second semantic retrieval, and containerized cloud deployments.
About Sai Pranav
Engineering Intelligence from Research to Production
Pranav's AI Philosophy
"State-of-the-art machine learning models are only as valuable as the pipelines and systems that deliver them into users' hands. I architect robust data flows, reliable embeddings, low-latency microservices, and continuous evaluation."
I completed my B.Tech in Artificial Intelligence & Machine Learning at Anurag University with an academic CGPA of 8.29 and an All India Rank of 5262 in GATE 2026 (Data Science & AI).
My journey spans building real-world AI assistants like the BigBasket SmartCart (leveraging FAISS, Cross-Encoders, and RAG to achieve 95% semantic retrieval accuracy with sub-2s latency) to designing automated MLOps pipelines for Customer Churn with MLflow, DVC, AWS S3, and GitHub Actions CI/CD to EC2.
Through internships at AI Varient, Unified Mentor Pvt. Ltd., and iNeuron Intelligence, I’ve delivered diagnostic ML pipelines achieving 93% recall on Alzheimer’s detection, employee retention models, and high-performance customer segmentation.
System Design
Modular APIs, Docker microservices, robust CI/CD.
Data Science
Advanced EDA, feature engineering & model interpretability.
Agentic LLMs
LangChain, FAISS, Pinecone, Prompt Engineering & RAG.
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Ask any question about Pranav's projects, experience, GATE rank, or MLOps architecture in natural language.
Portfolio & Case Studies
Featured Engineering Systems
Explore interactive AI applications, RAG pipelines, MLOps architectures, and deep learning models.
BigBasket SmartCart
AI-driven shopping assistant enabling semantic product search with 95% retrieval accuracy and ~2s latency using FAISS & Cross-Encoder re-ranking.
Netflix Customer Churn ML System
End-to-end MLOps pipeline predicting customer attrition with 99% recall. Integrated DVC data versioning, MLflow experiment tracking, and AWS S3 artifacts.
Agentic RAG Medical Assistant
Autonomous multi-agent medical question-answering system combining biomedical embeddings, recursive query rewriting, and factual citation verification.
Alzheimer's Disease Diagnostic Pipeline
Clinical ML pipeline reaching 93% recall rate using tuned Random Forest models, in-depth feature importance analysis, and clinical risk factor discovery.
AI Smarter SQL Analytics
Natural language to optimized SQL engine with database schema introspection, execution safety checks, and automatic query explanation.
AskTube – AI Video Insights
Deep semantic analysis of video audio streams, automated chapter creation, multi-lingual transcription, and conversational Q&A on video transcripts.
Agricultural AI Crop Recommender
Agronomic prediction model optimizing crop cultivation choices from soil Nitrogen, Phosphorus, Potassium (NPK), humidity, rainfall, and pH levels.
E-Commerce RNN Categorizer
Deep sequential neural networks (LSTM/GRU) for automated high-cardinality multi-class product classification on large e-commerce catalogs.
Chat_PDF – RAG Document Q&A
Interactive conversational document assistant with intelligent chunking, OpenAI embeddings, semantic retrieval, and conversational history memory.
Technical Competencies
Skills & Tech Ecosystem
Comprehensive toolkit across machine learning research, agentic architectures, and production MLOps.
MLOps & Cloud Infrastructure
Deployment & ExperimentationGenerative AI & LLMs
RAG & Vector RetrievalMachine & Deep Learning
Algorithms & ModelingComplete Tech Stack & Ecosystem
Track Record
Experience & Academic Background
Internships & Roles
Data Science Intern
AI Varient
- Engineered an ML pipeline for Alzheimer’s disease classification, achieving a 93% recall rate by building and hyperparameter tuning Random Forest models.
- Conducted comprehensive Exploratory Data Analysis (EDA) to uncover key predictive features, optimize data preprocessing workflows, and enhance overall model interpretability.
Data Science Intern
Unified Mentor Pvt. Ltd.
- Engineered features and optimized machine learning models to predict employee attrition, identifying key turnover drivers through data preprocessing.
- Designed analytics dashboards to visualize attrition patterns and presented data-driven recommendations to HR stakeholders for proactive workforce retention.
Machine Learning Intern
iNeuron Intelligence Pvt. Ltd.
- Conducted extensive EDA on large customer datasets to identify behavioral patterns and high-value customer clusters.
- Trained K-Means segmentation models achieving a Silhouette Score of 0.82 for targeted marketing strategies.
Education & Honors
All India Rank 5262
Graduate Aptitude Test in Engineering (GATE) – Data Science & Artificial Intelligence (DA) [93rd Percentile].
B.Tech in Artificial Intelligence & Machine Learning
Anurag University, Hyderabad, India
Class XII – MPC (Maths, Physics, Chemistry)
Sri Chaitanya Junior College, Hyderabad, India
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