Durga Prasad Vijji

About Durga Prasad Vijji

AI/ML Engineer with 6 years of experience in designing, building, and deploying machine learning and generative AI solutions, Strong expertise in Python-based ML development, NLP, deep learning, and end-to-end model deployment using modern frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face. Proven ability to deliver scalable AI solutions in cloud environments (AWS & Azure) with experience across data preprocessing, model training, evaluation, MLOps, and production deployment. Adept at collaborating with data engineers, product teams, and business stakeholders to translate complex requirements into impactful AI-driven solutions. experience designing, building, and deploying scalable ML and Generative AI solutions. Strong expertise in Large Language Models (LLMs), Prompt Engineering, Retrieval-Augmented Generation (RAG), and Python-based API development (FastAPI/Flask). Proven experience developing and deploying production-ready GenAI systems on AWS (SageMaker, EC2, EKS, S3, Lambda). Adept at collaborating with data scientists and engineering teams to deliver high-performance, secure, and scalable AI-driven applications.

Experience

1. Ai Ml Engineer

   PayPal

   Sep 2024 — Present · San Jose, CA, US

   Designed and developed machine learning models for fraud detection, transaction risk scoring, and customer behavior analysis using Python and Scikit-learn.• Built NLP pipelines using BERT and GPT-based models for text classification, alert summarization, and internal chatbot use cases.• Developed end-to-end ML workflows, including data preprocessing, feature engineering, model training, evaluation, and deployment.• Deployed ML models on AWS SageMaker and Azure ML, optimizing inference latency and scalability.• Implemented MLOps pipelines using MLflow, Docker, and CI/CD, enabling automated model retraining and version control.• Worked closely with data engineering teams to integrate ML models with Snowflake and cloud data pipelines.• Performed model monitoring, drift detection, and performance tuning to ensure reliability in production environments.• Collaborated with product and compliance teams to ensure models met enterprise security and regulatory standards.• Deployed scalable ML and GenAI workloads on AWS SageMaker and EC2, ensuring high availability and low latency.• Implemented model monitoring and logging using CloudWatch and MLflow.• Collaborated closely with data scientists to fine-tune transformer-based models and evaluate performance using regression and classification metrics.• Containerized applications using Docker and deployed on EKS (Kubernetes) for scalable production environments.• Deployed scalable ML and GenAI workloads on AWS SageMaker and EC2, ensuring high availability and low latency.• Implemented model monitoring and logging using CloudWatch and MLflow.• Collaborated closely with data scientists to fine-tune transformer-based models and evaluate performance using regression and classification metrics.• Containerized applications using Docker and deployed on EKS (Kubernetes) for scalable production environments.• Deployed scalable ML and GenAI workloads on AWS SageMaker and EC2, ensuring high availability and low latency.

Education

• Jawaharlal Nehru Technological University Hyderabad (JNTUH)

  Bachelor's degree

• University of North Texas

  Master's degree