Niteesh Bala Ganipisetty
Ai Ml Engineer (Sme) @Intuit
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WORK HISTORY
Ai Ml Engineer (Sme) @Intuit
US
Designed, developed, and deployed end-to-end machine learning pipelines using Python, Scikit-learn, TensorFlow, and cloud-native MLOps frameworks, delivering up to 92% model accuracy for risk prediction, forecasting, and customer analytics across Intuit’s financial platforms. • Applied advanced NLP and Computer Vision techniques (BERT, GPT, YOLO, OpenCV) to automate document classification, customer sentiment analysis, and image-based fraud detection, improving analytics and operational efficiency by 35%. • Built intelligent, NLP-driven solutions leveraging NLTK, spaCy, TextBlob, Hugging Face Transformers, and GPT-based LLM architectures to power AI chatbots, automated documentation, and user feedback analytics, enhancing customer engagement and automated support workflows by 40%. • Delivered high-impact data visualizations and interactive dashboards using Tableau, Matplotlib, and Seaborn to communicate predictive insights, model performance, and financial behavior analytics to product teams, data scientists, and leadership. • Architected and orchestrated AI agents utilizing LangChain, OpenAI APIs, and vector databases (Pinecone, FAISS) to automate workflows such as user inquiry classification, financial recommendation systems, and fraud triage, enabling autonomous decisionsupport capabilities across Intuit’s ecosystem. • Managed, optimized, and secured large-scale MySQL and structured/unstructured data repositories, ensuring compliant data preprocessing, model training, and evaluation pipelines for enterprise AI/ML deployments.
EDUCATION
Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science & Technology, Chennai
Bachelor of Technology, Computer Science Engineering
Grand Valley State University
Master of Science - MS, Computer Science
ABOUT NITEESH BALA GANIPISETTY
AI/ML Engineer (SME) with around 4 years of experience expertise in building end-to-end machine learning, deep learning, and Generative AI solutions across fintech, automotive, and enterprise domains. Experienced in developing NLP, Computer Vision, and LLM-driven applications using TensorFlow, PyTorch, Hugging Face, and GPT-based architectures, with a proven record of improving operational efficiency, predictive accuracy, and automation outcomes. Skilled in designing scalable data pipelines using Apache Spark, Hadoop, and Kafka, and deploying production-grade models on AWS, Azure, and GCP with robust MLOps, CI/CD, and containerization practices. Adept at transforming large, complex datasets into actionable insights through advanced analytics and visualizations, while optimizing model performance, cloud infrastructure, and AI-driven decision-support systems.
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