Production AI/ML Systems for Complex Data Workflows

Built AI/ML systems that transform noisy, unstructured, multimodal, and time-dependent inputs into structured outputs for downstream products and decision workflows. The work included data ingestion, model inference, validation, retrieval, tool execution, monitoring, and production deployment considerations such as latency, reliability, observability, and maintainability.

Focus areas: production inference, ML pipelines, model evaluation, structured outputs, workflow automation, observability

Technologies: Python, PyTorch, Docker, Kubernetes, MLflow, retrieval systems, sequence models, cloud/on-prem deployment patterns