I develop LLM-powered chatbots, RAG systems, and multi-agent AI applications using modern generative AI techniques. These solutions help clients automate knowledge retrieval, customer support, and internal processes with reliable, context-aware responses.
I design, build, and deploy predictive models for classification, regression, forecasting, risk scoring, and optimization problems. My work covers the full lifecycle—EDA, feature engineering, model training, validation, MLflow tracking, and production deployment.
I develop end-to-end ML pipelines on Azure, including data ingestion, feature engineering, model training, validation, deployment, and monitoring. These cloud-native pipelines ensure scalability, reproducibility, and seamless integration with enterprise data and applications.
I clean, transform, and analyze large structured and unstructured datasets to uncover actionable insights. My approach blends statistical rigor with practical business storytelling, helping teams make informed decisions backed by data.
I design and build multi-agent AI systems where specialized agents collaborate to automate complex workflows—such as document processing, analytics tasks, or multi-step reasoning. These solutions improve accuracy, reduce manual effort, and enable scalable, autonomous decision-making.
I implement automated ML pipelines, CI/CD workflows, and monitoring systems to ensure models remain stable and reliable in production. This includes model versioning, drift detection, retraining workflows, and robust deployment practices.
I develop NLP pipelines for text classification, sentiment analysis, document summarization, email/chat processing, and entity extraction. These solutions transform messy, unstructured text into structured intelligence that supports decision-making and automation.
I develop LLM-powered chatbots, RAG systems, and multi-agent AI applications using modern generative AI techniques. These solutions help clients automate knowledge retrieval, customer support, and internal processes with reliable, context-aware responses.
I design, build, and deploy predictive models for classification, regression, forecasting, risk scoring, and optimization problems. My work covers the full lifecycle—EDA, feature engineering, model training, validation, MLflow tracking, and production deployment.
I develop end-to-end ML pipelines on Azure, including data ingestion, feature engineering, model training, validation, deployment, and monitoring. These cloud-native pipelines ensure scalability, reproducibility, and seamless integration with enterprise data and applications.
I clean, transform, and analyze large structured and unstructured datasets to uncover actionable insights. My approach blends statistical rigor with practical business storytelling, helping teams make informed decisions backed by data.
I design and build multi-agent AI systems where specialized agents collaborate to automate complex workflows—such as document processing, analytics tasks, or multi-step reasoning. These solutions improve accuracy, reduce manual effort, and enable scalable, autonomous decision-making.
I implement automated ML pipelines, CI/CD workflows, and monitoring systems to ensure models remain stable and reliable in production. This includes model versioning, drift detection, retraining workflows, and robust deployment practices.
I develop NLP pipelines for text classification, sentiment analysis, document summarization, email/chat processing, and entity extraction. These solutions transform messy, unstructured text into structured intelligence that supports decision-making and automation.
