Gen AI/Machine Learning Engineer at Capital One

Designed and deployed GenAI and ML features to enhance customer experience with personalized content across enterprise platforms. Built scalable pipelines (Python, Pandas, NumPy, Scikit-learn) for data cleansing, feature engineering, model training, and real-time predictions. Implemented Retrieval-Augmented Generation (RAG) using OpenAI embeddings and FAISS; orchestrated LLM workflows with LangChain and LlamaIndex. Deployed LLM APIs as containerized microservices on OpenShift and established secure MLOps practices (IAM, Secrets Manager, VPC, KMS). Prototyped autonomous multi-agent LLM architectures for enterprise task orchestration.

A.I/M.L Engineer at Maxim Healthcare

Developed predictive models to prioritize healthcare outreach (churn, engagement, health conditions). Designed end-to-end ETL workflows, data preprocessing, and feature engineering. Built classification models (Logistic Regression, Random Forest, KNN) and performed NLP sentiment analysis to extract insights from clinician feedback. Created real-time analytics dashboards and lightweight microservices to support chatbot interfaces.

Senior Data Scientist at Amazon

Developed real-time fraud detection models using customer transactions, behaviors, and device metadata. Leveraged Databricks for scalable data exploration, feature engineering, and distributed inference; used SageMaker Pipelines and MLflow for reproducible deployments. Addressed class imbalance with SMOTE and under-sampling; conducted distributed training with PySpark MLlib and integrated ONNX for cross-platform inference. Built high-performance analytics with Apache Druid and orchestrated containerized microservices on Kubernetes/EKS.

Data Scientist at Western Alliance Bank

Collaborated with ML engineers to build risk analytics and customer insight models. Applied supervised and unsupervised methods (Logistic Regression, Decision Trees, K-Means) for credit risk scoring, transaction classification, and customer segmentation. Developed NLP-driven insights from customer feedback and service notes; created dashboards to monitor KPIs and model health.

Data Analyst at Cap Gemini

Translated business requirements into analytical solutions, applying regression, clustering, and classification to forecast sales, segment customers, and optimize operations. Built predictive models (R) with high accuracy, designed reusable Python/R analysis frameworks, and delivered interactive dashboards (Tableau) for stakeholders. Contributed to Agile teams and mentored colleagues on data science techniques.

Gen AI/Machine Learning Engineer at Capital One

Designed and deployed GenAI and ML-based features to improve customer experience through enhanced personalization and content relevance across enterprise platforms. Built scalable ML pipelines (Python, Pandas, NumPy, scikit-learn) for real-time predictions, including feature engineering, data cleansing, model training, and evaluation. Developed and fine-tuned classification and regression models for credit scoring, fraud detection, and risk profiling. Implemented Retrieval-Augmented Generation (RAG) workflows using OpenAI embeddings and FAISS for internal knowledge search; orchestrated LLM workflows with LangChain and LlamaIndex. Deployed LLM APIs as containerized microservices on OpenShift; integrated with Azure Function Apps and API Management for scalable GenAI services. Prototyped autonomous multi-agent task orchestration using CrewAI. Integrated OCR (Tesseract/Azure) for document understanding and connected to Azure OpenAI/Azure Cognitive Search. Configured GPU-backed training on

A.I/M.L Engineer at Maxim Healthcare

Developed and deployed predictive models to prioritize healthcare outreach by churn likelihood, health conditions, and engagement history. Led end-to-end data ingestion from Hadoop and third-party providers; built ETL pipelines; performed data preprocessing, imputation, encoding; conducted univariate/multivariate statistical analysis; used PCA for dimensionality reduction; built classifiers (Logistic Regression, Random Forest, KNN) for patient risk, appointment no-shows, and churn; performed NLP sentiment analysis on physician feedback; designed collaborative filtering recommendations for care programs; built real-time dashboards in Tableau/Matplotlib; created NodeJS services to proxy OpenAI/LangChain endpoints; integrated ASR/TTS (OpenAI Whisper, Tacotron) into LLM workflows; contributed to Agile transformation; prototyped speech-driven engagement tools.

Senior Data Scientist at Amazon

Built real-time fraud detection models using customer transaction history, device metadata; used Databricks for scalable data exploration, feature engineering, model training, and distributed inference. Built end-to-end ML pipelines with SageMaker Pipelines and MLflow for reproducible deployments. Addressed class imbalance with SMOTE and under-sampling; engineered features with PCA, t-SNE, and one-hot encoding. Distributed training with PySpark MLlib; integrated ONNX for cross-platform deployment. Developed high-performance log analytics with Apache Druid on Kubernetes; orchestrated containerized analytics microservices on EKS/Fargate. Implemented CI/CD with Jenkins; applied NLP techniques for sentiment analysis and fraud-alert validation; migrated on-prem workloads to Azure/Snowflake; GIS-based anomaly detection; dashboards in Tableau. Collaborated with security teams to align outputs with monitoring systems.

Data Scientist at Western Alliance Bank

Collaborated with ML engineers to build model training pipelines and conduct ML experiments for risk and customer analytics. Performed statistical modeling, data extraction, cleaning, and exploration of structured datasets. Applied supervised and unsupervised learning (e.g., Logistic Regression, Decision Trees, K-Means) to solve credit risk scoring, transaction classification, and customer segmentation. Built predictive models for branch performance and employee retention; developed NLP-based text mining solutions; designed dashboards in Tableau and R ggplot2 for leadership visibility; contributed to Agile processes and reusable Python/R scripts for domain problems.