Sarat Suhas Vijayababu
Senior Associate - Ai Engineer @Bank of America
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WORK HISTORY
Senior Associate - Ai Engineer @Bank of America
Jersey City, NJ, US
EDUCATION
Katz School of Science and Health
Master of Science - MS, Artificial Intelligence
SRM IST Chennai
Bachelor of Technology - BTech, Electronics and Communications Engineering
International Institute of Information Technology Bangalore
Postgraduate Diploma, Data Science
ABOUT SARAT SUHAS VIJAYABABU
AI/ML Engineer | Data Scientist | Building Scalable AI That Delivers Real Business ImpactI’m an AI/ML Engineer and Data Scientist passionate about turning complex data into actionable insights and building AI systems that truly drive measurable outcomes.With a strong academic foundation—B.Tech in ECE, a Postgraduate Diploma in Data Science, and an MS in Artificial Intelligence from Yeshiva University (GPA: 3.91)—I’ve led AI initiatives that saved over annually and optimized operations across industries.Highlights from my work at Getinge- Built SlideBot, an AI-powered presentation assistant that reduced slide creation time from 6 hours to under 10 minutes, automating 98% of manual work- Engineered a data analysis pipeline capable of processing over 1 million records per minute with 97% accuracy, saving more than 120 analyst hours monthly.Academic and independent projects- Delivered a video automation pipeline that reduced editing time by 45% using TimeSformer and Diffusion Models as part of my MS capstone project at Yeshiva University- Built ML-GPT, a custom large language model trained on curated Q&A pairs sourced from research papers, textbooks, and ML documentation, it leverages the Mamba architecture and was built with a custom neural network that achieved strong evaluation metrics with a BLEU score of 47.2, ROUGE-L score of 61.8, and BERT Score (F1) of 89.4, indicating high semantic and syntactic quality.Past impact in Singapore and India- Increased user engagement by 5x and improved sales by 12.8% at SAGE through data-driven digital strategy- Reduced IoT machine downtime by 40% using predictive maintenance models- Implemented RFID-based inventory systems that saved annually and improved supply chain visibility.Technical stack: Python, Azure OpenAI, LangChain, HuggingFace, Streamlit, MLOps, SQL, Pandas, Transformers, LLM fine-tuning, and cloud infrastructure.Let’s connect if you’re hiring, collaborating, or building scalable AI systems.Contact: v••••••••@gmail.com
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