As a Machine Learning Engineer at JPMorgan Chase, I build scalable Al solutions that enhance financial analytics, detect fraud, and streamline data-driven decision-making.

• Designed and optimized ML models that improved financial prediction accuracy by 35%, driving smarter business insights.

• Developed LSTM-based fraud detection models with 98% accuracy, strengthening compliance and transaction security.

• Boosted model performance by 10% through

BERT fine-tuning and GAN-based data augmentation for text analytics.

• Built CNN-powered anomaly detection systems, cutting fraudulent activities by 38% and improving transaction monitoring efficiency.

• Engineered FastAPI-based inference pipelines and deployed models on AWS (EC2, Lambda, SageMaker) and Google Cloud, reducing time-to-market by 30%.

• Automated ML workflows and leveraged time-series forecasting to optimize staffing, reducing operational costs by 30%.

At Accenture, I developed and deployed Al and deep learning solutions that optimized business operations, enhanced customer experiences, and improved system efficiency.

• Designed and fine-tuned ML and NLP models like

BERT, RNN, and CNN, achieving high accuracy in inten classification, anomaly detection, and sentiment prediction.

• Improved model efficiency by 38% through optimization, quantization, and pruning, enhancing performance across NLP and computer vision tasks.

• Built time-series forecasting models and XBoost pipelines to support business planning and operational decision-making.

• Deployed scalable models on AWS with CloudWatch monitoring, reducing issue resolution time by 30%.

• Developed an LLM-powered chatbot integrated with enterprise systems, increasing service efficiency by 25%.