Venkata Sai Chandra Veerlapally
Graduate Research Assistant Genai & Text Analytics @Georgia State University - J. Mack Robinson College Of Business
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WORK HISTORY
Graduate Research Assistant Genai & Text Analytics @Georgia State University - J. Mack Robinson College Of Business
Atlanta, GA, US
Built scalable data pipelines using Azure ML & CI/CD (GitHub Actions), reducing processing time by 40%• Designed production-scale RAG system enabling natural language querying, reducing retrieval time by 60%• Developed preprocessing pipelines (chunking, embeddings, metadata extraction), improving accuracy by 30%• Built evaluation framework (Recall@K, MRR), improving retrieval performance by 35%• Optimized LLM pipelines (Ollama + OpenAI), reducing inference cost by 30%• Improved model accuracy by 15% using advanced ML techniques
EDUCATION
Chaitanya Bharathi Institute Of Technology
Bachelor's degree, Mechanical Engineering
Georgia State University
Master of Science - MS, Data science and Analytics (Concentration in Big Data and Mahine Learning)
ABOUT VENKATA SAI CHANDRA VEERLAPALLY
Machine Learning Engineer & Data Scientist with experience building end-to-end AI systems across NLP, computer vision, and large-scale data pipelines.Currently a Graduate Research Assistant at Georgia State University, specializing in Retrieval-Augmented Generation (RAG), Generative AI, and scalable ML systems. Designed production-grade pipelines and AI solutions that improved retrieval efficiency by 60%, reduced latency, and enhanced model performance.Hands-on experience with Python, SQL, PyTorch, AWS, Azure, and Kubernetes, with a strong foundation in data engineering, distributed systems, and MLOps. Built real-time ML pipelines, federated learning systems, and advanced NLP models delivering measurable business impact.Previously worked at Ameris Bank and DRDO, developing ML-driven analytics solutions, automating data workflows, and enabling data-driven decision-making at scale.Actively seeking Software Engineering, Machine Learning, and Data Engineering roles focused on building scalable, production-grade AI systems.
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