Production AI systems, designed for outcomes.

Architected and delivered enterprise multi-agent products from zero to production for hospitality, sustainability, and regulated workflows.

• Scale8+ systems across hospitality, sustainability, public sector, and international engagements
• Quality95%+ retrieval accuracy with hybrid search
• Efficiency45% lower latency and 35% lower token usage
• TopologySupervisor + workers + persistent state
• StackLangGraph, LiteLLM, PostgreSQL, MLflow, LangFuse
• InfraDockerized deployment on Azure with CI/CD pipelines

Built a multi-agent automation suite using RPA and Playwright to automate declarations and reporting workflows for law and banking sector land registry operations.

• DomainLaw, banking, and land registry process automation
• AutomationRPA + Playwright agents for secure data capture, verification, and document generation
• ComplianceEnd-to-end reporting with audit-safe execution and stakeholder-ready outputs
• StackPython, RPA, Playwright, LLM, LangGraph, FastAPI, PostgreSQL

Delivered a scalable supply chain optimization engine using machine learning and statistical modeling, with production-ready MLOps for inference and retraining.

• ProblemSupply chain optimization with demand forecasting, routing, and inventory analytics
• ModelingStatistical ML models and end-to-end inference pipelines
• MLOpsProduction deployment, autoscaling task execution, and MLflow experiment tracking
• StackPython, PostgreSQL, Celery, RabbitMQ, Azure, Docker, MLflow

Contributed and refactored a public-sector control tower with a Django API backend and an RShiny frontend, delivering data-driven dashboards and stakeholder reporting.

• VisibilityCentralized analytics and decision support for policy and operations teams
• ArchitectureDjango API backend powering RShiny frontend dashboards and interactive workflows
• DeliveryScalable public-sector reporting with secure deployment and automated data refresh
• StackDjango, RShiny, NGINX, Auth0, Docker, Azure