AI/ML Engineer at Johnson & Johnson

Led AI/ML initiatives using multimodal data (MRI/CT imaging, clinical, genomic) to support drug development and biomarker discovery. Standardized medical imaging data across devices, improving model accuracy by 20%. Built and deployed an XGBoost model for patient-outcome prediction (clinical/genomic/imaging data), increasing accuracy by 30%. Implemented a RAG pipeline indexing clinical trial documents with FAISS and GPT-3 for real-time researcher Q&A. Developed CNN-based imaging models (PyTorch/TensorFlow) and LangChain-driven RAG workflows for knowledge retrieval. Used PySpark for distributed preprocessing, cutting data prep time by 30%. Fine-tuned BERT for entity extraction and summarization; generated medical text with GPT-3. Established reproducible MLOps with MLflow, Docker, Kubernetes; deployed on AWS Bedrock.

Data Scientist (AI/ML) Engineer at US Bank

Developed ML-enabled automation for financial reconciliation across global BPO/shared services; integrated with SmartStream TLM. Built time-series forecasting pipelines (Prophet, LSTM) improving accuracy by 35%. Applied Isolation Forest with SMOTE for anomaly detection and tuned with grid search. Built end-to-end ML pipelines with Airflow, Spark, and Kafka/Spark Structured Streaming; designed ETL/ELT with SQL and PostgreSQL. Implemented observability with CloudWatch, Prometheus, and Grafana; added model-drift monitoring. Created low-latency inference and data-validation steps; standardized MLOps with MLflow, Docker, and CI/CD; improved training throughput by 40% through feature engineering.

Senior Machine Learning Engineer at State of VA

Developed and deployed full-stack Django/AngularJS apps with scalable REST APIs. Automated data ingestion from MySQL and Excel; built ML models for property usage forecasting, boosting accuracy by 30%. Automated feature engineering and data ingestion pipelines with Pandas; integrated Azure Data Factory for ETL/ELT. Created Power BI dashboards translating analytics into actions. Docker/Kubernetes deployments with Jenkins and GitHub Actions; asynchronous processing boosted throughput by 20%. Implemented CI/CD, testing strategies, and secured APIs with JWT-based access.

Data Scientist at CorEvitas

Implemented MVC architecture with Django/Flask; built high-performance web apps and RESTful APIs. Developed multi-threaded Python wrappers to accelerate data ingestion/processing by over 50%. Migrated data from SQLite to MySQL/PostgreSQL; performed PySpark transformations and stored results in AWS S3; used MongoDB for JSON data. Implemented unit/integration tests, API security (JWT, RBAC, CORS), and maintained clean codebase. Deployed on AWS Elastic Beanstalk and Azure Blob Storage; established monitoring and logging.

Python Developer at Citi Bank

Developed RESTful APIs and backend components using Django/Flask; implemented JWT-based authentication, RBAC, and CORS for external partner integrations. Built dynamic templates and backend interfaces; collaborated with frontend teams to deliver robust APIs. Designed PostgreSQL/MySQL schemas; migrated legacy data and integrated SOAP/XML services. Contributed to performance improvements and code quality through testing and version control; participated in Agile ceremonies; implemented CI/CD pipelines using Jenkins and GitHub.

AI/ML Engineer at Johnson & Johnson, New Brunswick, NJ

Utilized imaging data (MRI, CT), clinical and genomic data to develop AI-powered solutions that support data-driven drug development and biomarker discovery. Standardized and normalized medical image data from MRI and CT scans, reducing equipment-related inconsistencies and improving data consistency, which contributed to model performance gains. Developed and deployed an XGBoost model for predicting patient outcomes using multimodal data, enhancing prediction accuracy. Designed and deployed modular RAG pipelines indexing clinical trial documents with FAISS and integrating GPT-3 for real-time Q&A for researchers. Built production-grade ML workflows with PyTorch/TensorFlow, LangChain, and an orchestration layer to support agent-based tasks. Leveraged AWS Bedrock to integrate foundation models and deployed models on EC2/SageMaker. Used PySpark for distributed data processing and optimized SQL pipelines for large clinical/genomic datasets.

Data Scientist (AI/ML) Engineer at US Bank, New Jersey

Led ML-enabled automation for financial reconciliation across global BPO/shared services, integrating with SmartStream TLM for automated exception handling. Built time-series forecasting systems (Prophet, LSTM), improving forecast accuracy by about 35%. Implemented an Isolation Forest for anomaly detection with SMOTE for class balance, tuned via grid search. Architected end-to-end ML pipelines with Airflow, Spark, and Kafka(Spark Structured Streaming) to support batch and real-time workflows. Designed ETL/ELT processes in SQL (PostgreSQL) and integrated diverse financial systems. Added observability stacks with CloudWatch, Prometheus, and Grafana, enabling proactive monitoring and reducing downtime by 30%. Implemented drift monitoring and automated data validation to ensure reliable production inference. Reduced model training time by 40% through feature engineering optimizations; standardized MLflow-based CI/CD for repeatable deployment. Built low-latency inference services and ensure

Senior Machine Learning Engineer at State of VA, Richmond, VA

Developed full-stack web applications using Django and AngularJS to deliver dynamic dashboards and reporting, boosting user engagement by around 40%. Built scalable RESTful APIs and ML models for property usage forecasting (scikit-learn/XGBoost), improving reporting accuracy by 30%. Automated feature engineering and integrated ML outputs into live workflows, reducing manual analysis by 40%. Automated data ingestion pipelines unifying MySQL and Excel-based sources with Pandas, cutting reconciliation effort by 60%. Migrated data into Azure Data Factory, performed data cleaning, and created Power BI dashboards with Python automation. Orchestrated Python apps with Docker and Kubernetes, enabling scalable cloud deployments. Implemented CI/CD (Jenkins, GitHub Actions) and improved page performance via asynchronous processing. Implemented monitoring and alerting for model drift and data quality, improving production reliability.

Python Developer at Citi Bank, India

Developed RESTful APIs and backend components with Django and Flask, implementing secure JWT-based authentication and CORS for external partners. Built dynamic templates and UI endpoints, collaborated with front-end teams to tailor APIs for rich UIs, and designed normalized PostgreSQL/MySQL schemas. Migrated legacy data sources, including SOAP/XML endpoints, to modern architectures and ensured data integrity. Created and maintained unit tests and integrated security practices. Delivered scalable backend services with Python, Django, and SQL, and collaborated with QA and DevOps to ensure smooth releases. Worked with AWS deployment on EC2/LB environment and Docker-based workflows.

Data Scientist at CorEvitas, Waltham, MA

Implemented MVC architecture for web apps using Django/Flask, enabling scalable data-driven tooling. Built high-performance multi-threaded wrappers to accelerate data ingestion and processing by over 50%. Migrated SQLite to MySQL/PostgreSQL with scripted migrations; designed RESTful APIs, JWT-based security, and API testing. Used PySpark on Databricks for large-scale data transformations, with outputs stored in AWS S3. Employed MongoDB for unstructured data and developed dashboards with Python, Bootstrap, and JavaScript. Developed unit/integration tests with PyTest, enforced code quality, and collaborated across teams using Agile practices. Automated data ingestion pipelines, implemented ETL pipelines in cloud environments (AWS, Azure), and built BI dashboards with Power BI.