Senior Data Engineer/ML/AI at Johnson & Johnson

Designed and deployed LLM-powered AI systems using Azure ML Studio and Databricks, integrating Retrieval-Augmented Generation (RAG) for contextual text generation and search. Fine-tuned BERT, RoBERTa, and T5 models for text summarization and semantic search; built prompt engineering frameworks to reduce hallucinations and improve factual reliability. Built end-to-end ML pipelines with Airflow and Spark, deployed containerized models on Kubernetes and Kubeflow, and implemented LangChain-based LLM orchestration for multi-model inference. Monitored model performance with Prometheus and Grafana; developed GANs and diffusion models for content generation; created Power BI dashboards; developed REST APIs with Flask/FastAPI for real-time inference. Led cross-functional collaboration and authored design docs.

Data Engineer/AI/ML at State Street

Built and deployed transformer-based search and anomaly detection solutions (BERT, T5) for semantic search and document similarity in financial datasets. Implemented RAG-based pipelines with LangChain and FAISS for real-time retrieval and grounded responses. Developed AI-driven fraud detection with unsupervised/semi-supervised techniques and deployed LLM inference endpoints on AWS SageMaker with CI/CD. Implemented explainable AI reporting using SHAP and LIME; built PySpark and Kafka pipelines for real-time feature streaming; automated dashboards for model metrics, drift, and retraining triggers. Deployed APIs and administered containerized ML services on AWS; contributed to CI/CD and documentation.

Data Engineer/ML at Tata Motors

Designed predictive maintenance solutions using time-series forecasting and deep learning (RNN/LSTM) on sensor data; built ML workflows on Azure Databricks and Kubernetes for high availability and automated scaling. Created Power BI dashboards and R Shiny dashboards for monitoring, and developed ETL automation scripts for model retraining and reporting. Implemented Docker-based containerization and CI/CD with Git/Jenkins; collaborated on Azure Monitor dashboards for drift tracking; optimized costs with autoscaling and spot instances; integrated ML outputs into dashboards for leadership reviews.

Data Analyst at Croma

GSS gathered requirements for data migration and reporting enhancements; designed scalable ETL workflows and dashboards in Tableau and Power BI; created SQL-based data validation scripts and exception reports; implemented AWS RDS, EC2 for ETL workflows; built dynamic dashboards and reports; developed modular Python code for API/data extraction; collaborated with cross-functional teams on data governance and vendor management; established monitoring dashboards for model performance and usage.

Senior Data Scientist/ML/AI at Johnson & Johnson

Lead end-to-end development of AI/ML solutions on Azure Machine Learning Studio, automating workflows for data ingestion, model training, validation, and deployment. Designed predictive analytics and anomaly detection models (Random Forest, XGBoost, SVM) to improve operational KPIs. Applied NLP techniques (SpaCy, NLTK) to extract insights from large unstructured datasets. Implemented scalable ML orchestration with Kubernetes and Kubeflow, enabling reproducible pipelines across multiple production environments. Designed and deployed RESTful APIs using Flask and FastAPI for real-time model inference integrated into enterprise applications. Conducted A/B testing and statistical validation to quantify business impact. Built real-time network analytics dashboards with Power BI and Azure Databricks to monitor key performance indicators for leadership. Led development of generative AI models (GANs, diffusion models) for simulation and synthetic data to support predictive maintenance. Collabor

Data Scientist/ML/AI at State Street

Built automated ML pipelines using AWS Step Functions and AWS Glue to process terabytes of data for customer segmentation, fraud detection, and network optimization. Designed 4G/5G network performance dashboards in Tableau and Alteryx; visualized KPIs such as latency, throughput, and packet loss across regions. Conducted large-scale EDA with PySpark to uncover trends supporting capacity planning. Applied deep learning (TensorFlow, PyTorch) for time-series forecasting and anomaly detection in network traffic. Built explainable AI using SHAP and LIME to interpret complex models for regulatory transparency. Deployed containerized ML environments with Docker and Kubernetes to ensure high availability for inference services. Integrated AWS Redshift, Athena, and Snowflake for scalable analytics querying. Translated technical insights into actionable strategies with cross-functional teams; maintained system configurations and documentation. Automated Tableau extracts and dashboards; participa

Data Scientist/ML at Tata Motors

Designed cloud data architectures using Azure SQL and Databricks to support manufacturing and IoT sensor data analytics. Developed predictive maintenance models using time-series forecasting to minimize downtime of connected vehicle systems. Automated ETL and model retraining workflows with cron jobs and Python scheduling, and used Azure Data Factory. Created interactive R Shiny dashboards and Power BI visuals to monitor operational metrics. Deployed Spark applications with autoscaling; integrated CI/CD with Git and Jenkins for reproducible deployments. Connected Power BI with BigQuery for near-real-time analytics across departments. Served as liaison between IT and business units to ensure timely delivery and change management. Implemented comprehensive monitoring with Azure Monitor and Log Analytics for observability. Leveraged PyTorch DataLoader for efficient data preprocessing; built scalable ML pipelines on Kubernetes. Documented architectures, training methodologies, and deployme