Sai Bhargav Reddy

About Sai Bhargav Reddy

I’m a results-driven Machine Learning Engineer with over 5 years of experience designing, developing, and deploying end-to-end AI/ML solutions across finance, marketing, and customer analytics domains.My expertise lies in building scalable, explainable, and production-ready ML systems using a wide range of tools and platforms from Python, PySpark, and SQL to cloud-native technologies like Azure ML, AWS SageMaker, and Databricks.At Equifax, I led the development of real-time fraud detection pipelines, NLP-based automation systems, and MLOps frameworks that cut manual processing by 35%, reduced risk by 50%, and accelerated deployment cycles by 40%. Prior to that, I built predictive analytics models and deep learning-based chatbots at Tata Consultancy Services, improving customer segmentation, demand forecasting, and resolution efficiency.My technical toolkit includes:ML/DL: XGBoost, LightGBM, TensorFlow, PyTorch, Scikit-learnNLP: BERT, GPT-2, LSTMs, Hugging Face, spaCyMLOps: MLflow, GitHub Actions, Docker, KubernetesBig Data & Cloud: Azure, AWS, Snowflake, Kafka, DatabricksI’m passionate about combining AI with real-world business impact especially in high-stakes environments like fraud detection and risk analytics and always focused on building transparent, explainable systems using SHAP, LIME, and Integrated Gradients. Let’s connect if you\'re interested in:Collaborating on cutting-edge ML/NLP projectsBuilding scalable, interpretable AI solutionsOpportunities at the intersection of AI, cloud, and finance

Experience

1. Machine Learning Engineer

   Equifax

   Jan 2024 — Present · US

   Led the end-to-end lifecycle of ML models, from data preprocessing and feature engineering to model deployment, leveraging XGBoost, LightGBM, and TensorFlow, which improved underwriting accuracy by 25% and significantly reduced false positives in risk assessments.• Developed and implemented scalable MLOps pipelines integrating CI/CD workflows (GitHub Actions, MLflow), Docker, and Kubernetes, automating model versioning, continuous deployment, and performance monitoring, reducing deployment cycle times by 40%.• Engineered an NLP-based risk profiling system using Transformer architectures (BERT, LSTMs, GPT) to automate policy evaluations, improve fraud detection precision, and accelerate document processing by 35%, minimizing manual intervention.• Built a real-time fraud detection framework by deploying unsupervised learning techniques (Isolation Forest, Autoencoders, DBSCAN) combined with Apache Kafka and Snowflake, enhancing transaction monitoring and mitigating financial risk by 50%.• Constructed and deployed machine learning models on Microsoft Azure using Azure ML, Blob Storage, and Azure Kubernetes Service, improving model management, scalability, and reducing deployment time by 40%.• Managed the development of explainable machine learning solutions on Databricks, integrating SHAP, LIME, and integrated gradients into production pipelines to enhance model transparency, support regulatory compliance, and build stakeholder trust in high-stakes financial systems.

Education

• Teegala Krishna Reddy Engineering College

  Bachelor of Technology - BTech, Computer Science

• Concordia University-St. Paul

  Master's degree, Data Analytics