- Working on portfolio allocation and optimization using reinforcement learning models like PPO, DDPG, and other actor-critic algorithms. - Focused on applying machine learning in quantitative finance, leveraging unsupervised learning and Ray Tune for hyperparameter tuning and distributed GPU training.- Tools include Stable Baselines, OpenAI Gym, PyTorch, Ray, MLflow and others

Developed Scalable Predictive Models for Outage Detection- Led the development of a scalable ML/AI Proof of Concept (POC), utilizing sequential models like RNN and LSTM, along with statistical models such as SARIMA, to predict network outages using PyTorch.- Performed data analysis, feature engineering, and anomaly detection on unstructured data from various sources. Cleaned, transformed, and prepared datasets for model training.- Designed an end-to-end MLOps pipeline, orchestrating real-time DNS monitoring using Power BI and optimizing data ingestion into Azure Table Storage.- Achieved a 300% improvement in script efficiency through multithreading, delivering actionable insights to 10+ non-technical stakeholders and significantly enhancing operational performance.

• Engineered 50+ automation scripts in Python and JavaScript, utilizing Git to enhance testingefficiency by 40% with TestComplete/Selenium and optimize critical processes.• Analyzed project requirements and tracked quality metrics in Azure DevOps for a robust SDLC.

CSI 2120 Programming Paradigms

• Managed server-side Node.js API operations and implementing Python automation for dataretrieval. Developed familiarity with MongoDB, Kafka, Postman, ElasticSearch, and Docker.

data analysis