Bhargavi Reddy Alumolu
Ai Ml Engineer @BNY
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WORK HISTORY
Ai Ml Engineer @BNY
TX, US
Performed feature engineering and data preprocessing for machine learning models using Python and Pandas, applying recursive feature elimination (RFE) on trading transaction data to improve model accuracy by 15% and reduce feature dimensionality by 30%. • Engineered classification models using Scikit-learn and PyTorch with Random Forest and XGBoost to predict trade settlement likelihood, achieving 87% AUC-ROC score and streamlining compliance review workflows for operations teams. • Built AI-powered client risk assessment system using Python, Pandas, and logistic regression with survival analysis to analyze portfolio holdings and market exposure history, enabling risk management for 80 high-value investment banking clients. • Developed predictive analytics pipeline using XGBoost regression and feature engineering in Python to forecast client portfolio risk exposure, improving capital allocation accuracy by 20% for quantitative research teams and reducing pricing error rate by 12%.• Deployed inference APIs using FastAPI and Docker on AWS EC2, integrating OpenAI and Hugging Face APIs for trade document summarization, achieving 99% uptime with scalable low-latency inference in a production environment.
EDUCATION
Anurag Group of Institutions
B.tech, Computer Science Engineering
University of North Texas
Master of Science - MS, Data Science
ABOUT BHARGAVI REDDY ALUMOLU
AI/ML Engineer | Machine Learning | Generative AI | MLOps | AWS | NLP | LLMsAI/ML Engineer with 3+ years of experience building, deploying, and scaling production-grade machine learning models and AI-driven applications. Strong expertise in Machine Learning, Deep Learning, Natural Language Processing (NLP), and Generative AI, with hands-on experience across the full ML lifecycle—from data preprocessing and feature engineering to model deployment and monitoring.Core Skills & Keywords:Machine Learning | Deep Learning | Data Science | NLP | Generative AI | Large Language Models (LLMs)| Retrieval-Augmented Generation (RAG)| Prompt Engineering | Feature Engineering | Model Optimization | Explainable AI (XAI)| SHAP | Supervised & Unsupervised Learning | Classification | Regression | Clustering | Anomaly DetectionTech Stack:Python | SQL | Pandas | NumPy | Scikit-learn | TensorFlow | PyTorch | Keras | XGBoost | LightGBM | Hugging Face | Transformers | OpenAI API | LangChain | LangGraphCloud & MLOps:AWS (SageMaker, S3, EC2, Lambda)| Azure (Data Factory, Databricks, Data Lake)| Docker | Kubernetes | FastAPI | MLflow | CI/CD Pipelines | Model Deployment | API Development | Microservices ArchitectureDatabases & Tools:MySQL | PostgreSQL | SQL Server | Vector Databases (FAISS, Pinecone, ChromaDB)| Power BI | TableauProfessional Impact: Improved model accuracy by 15% using advanced feature engineering and model tuning Built scalable ML pipelines and real-time inference systems with sub-100ms latency Developed risk assessment and predictive analytics models for financial services Designed recommendation systems and pricing models driving revenue growth Deployed AI solutions with 99% uptime using cloud-native architecturesPassionate about leveraging AI, Machine Learning, and Generative AI to solve complex business problems, optimize decision-making, and build intelligent, scalable systems.Open to opportunities in AI/ML Engineering, Data Science, and Generative AI. Let’s connect and collaborate!b••••••••@techjobmail.com94••••••23
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