AI Engineer at American Express

Built and deployed enterprise-scale forecasting and analytics pipelines using Python, PyTorch, TensorFlow, PySpark, AWS Glue, SageMaker, Redshift, and MLflow. Architected and productionized an enterprise RAG knowledge assistant using GPT-4, LangChain, FAISS, Pinecone, and OpenAI APIs. Fine-tuned transformer models and improved document classification and extraction. Engineered MLOps infrastructure with MLflow, Docker, Kubernetes, Terraform, Jenkins/GitHub Actions, Prometheus, Grafana, and Evidently AI for deployment automation, monitoring, drift detection, and retraining. Optimized GPU inference with CUDA/TensorRT/quantization/distillation. Built a production-grade document intelligence platform using OCR, Hugging Face Transformers, SageMaker, and vector databases for classification and entity extraction workflows.

AI Engineer at CVS Health

Designed and deployed generative AI/LLM applications using Python, LangChain, OpenAI/Azure OpenAI, RAG, and vector databases (Pinecone/FAISS) for clinical document intelligence and patient support. Built end-to-end ML pipelines for ingestion, feature engineering, training, evaluation, and deployment with PyTorch/TensorFlow/Scikit-learn, MLflow, Docker, Kubernetes, and CI/CD for scalable healthcare AI. Developed AI agents and FastAPI microservices integrating EHR and clinical data sources on AWS services (S3, Lambda, Bedrock, SageMaker). Implemented prompt engineering, evaluation, guardrails, Responsible AI, and monitoring/drift detection with compliance collaboration. Created Streamlit dashboards for benchmarking and developed NLP pipelines for clinical text classification research. Containerized and deployed microservices on Docker/EC2 with CI/CD for zero-downtime updates.

Data Scientist at Paytm

Engineered event-driven AI data platforms using Kafka, FastAPI, Elasticsearch, and REST microservices for high-volume transaction processing with strong uptime. Built scalable feature engineering and model inference pipelines with Spark/PySpark, SQL, Airflow, reinforcement learning, and bandit algorithms to improve recommendations and engagement. Developed ML models for demand forecasting, delivery time prediction, retention, and recommendations using Python, SQL, Pandas/NumPy, Scikit-learn, and XGBoost. Designed scalable ETL workflows with Apache Spark and Airflow on AWS/GCP. Produced dashboards and KPI reporting with Tableau/Power BI. Collaborated with product/engineering on predictive models, A/B testing, feature engineering, and pricing/promotion optimization. Implemented NLP/semantic search automation and embeddings-based workflow automation. Containerized deployments and CI/CD with Docker, Kubernetes, Jenkins, GitHub Actions, and Terraform to reduce deployment failures and improv