Generative AI Engineer at Citizens Financial Group

Designed and deployed a Generative AI-powered financial document intelligence platform, leveraging fine-tuned LLMs (GPT, LLaMA) to automate credit risk analysis, fraud detection, and compliance checks. Implemented Retrieval-Augmented Generation (RAG) pipelines with FAISS, Pinecone, and ElasticSearch for domain-specific knowledge retrieval, improving accuracy of financial reporting and document summarization. Built end-to-end ML pipelines on AWS SageMaker and Glue for ingesting, cleaning, and analyzing structured/unstructured banking datasets. Fine-tuned domain-specific LLMs using LoRA, PEFT, and instruction tuning for regulatory reporting, KYC/AML verification, and internal knowledge search. Implemented explainable AI frameworks; ensured compliance. Optimized LLM inference with AWS Inferentia and SageMaker Neo for low-latency apps. Developed ETL and real-time data pipelines using Apache Spark, Glue, and Kafka. Applied data augmentation to improve model robustness. Established MLOps pra

Generative AI Engineer / Data Scientist at Centene Corporation

Designed and implemented Generative AI–based healthcare virtual assistant (GPT-3/transformers) to automate claims inquiries, benefit explanations, and provider interactions. Built Retrieval-Augmented Generation (RAG) pipelines with FAISS and Elasticsearch for knowledge retrieval from policy documents, clinical guidelines, and member communication archives. Built NLP/NLU models for intent detection, entity recognition, and contextual response generation. Automated summarization workflows for clinical notes, authorization forms, and case management reports. Implemented speech-to-text and text-to-speech to support multilingual member engagement. Built end-to-end ML pipelines on Google Cloud Vertex AI and BigQuery; automated data ingestion with Airflow and Spark. Implemented MLOps with MLflow; ensured HIPAA compliance; integrated OpenAI GPT APIs with internal systems; developed predictive analytics models for churn, care gaps, and plan optimization. Collaborated with clinical teams to en

Machine Learning Engineer at Port Authority of NY/NJ

Developed predictive maintenance and anomaly detection for IoT and operational telemetry; built time-series forecasts (ARIMA/LSTM), random forest, gradient boosting for degradation patterns, and proactive maintenance decisions. Supported AWS-based ML workflows with EC2, S3, and ECS; built streaming and batch inference pipelines with Kafka and Spark Streaming; created containerized services with Docker and Flask APIs for internal consumption; implemented NLP/text mining on technician notes using SpaCy; created dashboards in Power BI to surface maintenance risk indicators; integrated ML models with ticketing and monitoring systems; contributed to DevOps practices with CI/CD and versioning.

Data Scientist at Walmart

Led ML pipelines on Azure Databricks for demand forecasting and inventory optimization; built models using Regression, Random Forest, XGBoost, and LSTM; improved stock stability and replenishment decisions. Established MLOps using MLflow, Azure Data Factory, Airflow; containerized models with AKS; built retrieval and search for product discovery; implemented price optimization models; real-time store analytics with Kafka; forecasting for utilities; connected ML predictions with ERP/MES/inventory platforms; produced leadership dashboards in Tableau and Google Data Studio; supported analytics modernization for scalable ingestion and distributed ML workloads.