Khuram Ismael

Led design and development of ML models for predictive analytics, anomaly detection and recommendation systems using TensorFlow, PyTorch, Scikit-Learn, and CNN/RNN architectures. Architected and deployed ML pipelines on Kubernetes and Docker for scalable cloud-native solutions; integrated Kafka, Spark and n8n for real-time data processing and automated workflows. Implemented front-end visualization with React.js, built data dashboards, and deployed APIs with Flask and FastAPI. Applied feature engineering, hyperparameter tuning, and reinforcement learning to improve model accuracy and dynamic decision-making. Led multi-agent systems and LLM orchestration for enhanced GenAI applications; managed model deployment with MLflow and Kubeflow; designed cloud-native infrastructure on AWS, GCP, and Azure with SageMaker, Glue, and Terraform. Established CI/CD pipelines with Jenkins, GitHub Actions and CircleCI; built scalable NoSQL and relational DB solutions (MongoDB, Cassandra, DynamoDB, Snowfl

Designed ML models for customer segmentation, recommendations, and demand forecasting to boost outcomes in retail and e-commerce. Built real-time data pipelines with Python, Apache Kafka and Spark; automated data workflows with n8n; integrated NLP models like BERT and SpaCy for sentiment analysis and chatbots; developed RESTful APIs with Node.js for real-time ML inference; used speech-to-text for real-time transcription; optimized models for low latency on large datasets; created React.js dashboards for stakeholders; conducted A/B testing and cross-validation; integrated NLP solutions to automate content analysis and text workflows; updated models regularly to adapt to new data and maintain accuracy.

Delivered full-stack web apps using Node.js, React.js, and MongoDB; built RESTful APIs and integrated ML models into production systems; automated data workflows; implemented NLP features and chatbots using BERT and SpaCy; created dashboards with React for stakeholders and performance dashboards; optimized ETL pipelines with Python and SQL; enabled real-time ML inference via API endpoints; ensured responsive, cross-device UI.