Sayyid Rizvi

I am an AI/ML and Data Science Engineer with over 8 years of experience delivering intelligent, production-grade systems across healthcare, fintech, and enterprise SaaS. I specialize in the full ML lifecycle from data acquisition and feature engineering to deployment, monitoring, and retraining.

I build and fine-tune LLMs and domain-specific Transformers and design NLP pipelines using Hugging Face, LangChain, spaCy, and BioBERT, with hands-on deployment on AWS SageMaker, Azure ML, and Google Cloud Vertex AI. My work has produced measurable results, including a BERT-powered KYC parser that reduced manual review time by 60% and a real-time fraud pipeline that improved detection latency by 70%.

I lead MLOps automation and observability efforts, implementing MLflow, Docker, Kubernetes, GitHub Actions, and Azure DevOps to shorten deployment cycles from weeks to days and enable proactive retraining via Evidently and Azure Monitor. In healthcare projects I improved ICU deterioration prediction accuracy by 18% and boosted clinical note classification F1 by 12% with BioBERT.

I combine rigorous data engineering with responsible, compliance-driven AI (HIPAA, SOC 2, GDPR, Responsible AI) to build scalable, high-performance pipelines. I enjoy translating complex business requirements into impactful ML solutions that improve decision-making and operational efficiency.