● Designed credit-risk feature pipelines in Python and SQL on AWS, improving training data consistency and enabling faster, auditable model iteration across governance checkpoints enterprisewide.

● Engineered PyTorch classification models with MLflow tracking, delivering explainable predictions through REST APIs and reducing manual underwriting review effort for frontline analysts daily consistently.

● Optimized SageMaker training jobs with GPU utilization and data sharding, shortening experimentation cycles and supporting timely regulatory model validation submissions with reproducible artifacts quarterly.

● Automated CI/CD workflows in GitHub and Docker, packaging inference services for Kubernetes deployment and improving release reliability across development, test, and production environments significantly.

● Validated model performance with A/B testing and drift monitoring, surfacing degradation early and protecting portfolio outcomes through controlled rollback, recalibration, and alerting processes proactively.

● Integrated clinical NLP pipelines with Transformers and Scikit-learn, extracting entities from notes and improving downstream analytics accuracy for care-operations stakeholders across facilities nationwide systemwide.

● Streamlined ETL pipelines in Airflow and Databricks, consolidating disparate datasets and enabling reproducible feature engineering for predictive readmission risk models at scale reliably consistently.

● Configured secure FastAPI services on Azure, exposing batch scoring endpoints and improving interoperability with hospital applications through documented API contracts and authentication controls end-to-end.

● Analyzed model fairness and bias metrics with Python, aligning thresholds with policy guidance and supporting transparent reporting for compliance and clinical leadership reviews routinely.

● Standardized MLOps runbooks with MLflow and Git, clarifying deployment steps and reducing on-call incidents during model releases across multiple teams and environments measurably operationally.

● Implemented retrieval augmented generation solutions with LangChain and vector databases, improving knowledge access for support agents and reducing time-to-resolution on complex tickets substantially measurably.

● Orchestrated multi-agent workflows with LangGraph and CrewAI, coordinating tool calls and improving automation reliability for enterprise document processing tasks across shared services securely consistently.

● Refactored data services with Node.js and PostgreSQL, enhancing throughput for feature stores and enabling scalable training data access for ML pipelines reliably continuously.

● Deployed containerized workloads with Docker, Terraform, and AWS, provisioning repeatable environments and accelerating onboarding for new project teams, stakeholders, and delivery timelines quickly operationally.

● Monitored production inference with logging, observability, and model monitoring, tracing failures quickly and maintaining service SLAs for client-facing AI applications continuously at-scale reliably systemwide.