FORWARD-DEPLOYED CLIENT ENGAGEMENTS
Ultra OT (occupational therapy practice)
2026
Lead Engineer — onsite client engagement
- Engaged onsite with a practice that had no single view of its operations — case status, scheduling and billable time lived across spreadsheets, calendars, and email. Ran discovery with clinicians and admin staff to map the real workflow before committing to a build.
- Scoped the smallest useful release first — case flow and Jira-style ticketing — then grew it into the practice's full internal operations system as sole engineer: calendar scheduling, client intake imports, and time/finance visibility (Next.js, TypeScript, Supabase).
- Solved their hardest scheduling problem by clustering clients geospatially on driving distance via the Google Routes API, turning ad hoc visit planning into route-aware scheduling; iterated on it in place as the team used it.
Termly (healthcare)
2026
Lead Engineer — onsite client engagement, regulated healthcare
- Delivered a document AI system for Australian medical contract automation, replacing slow and inconsistent manual contract review in a regulated healthcare setting.
- Built the full pipeline as sole engineer: scanned PDF extraction, OCR correction, structured clause and entity extraction, Medicare/PBS identifier validation, and automated risk scoring (FastAPI/Pydantic, Claude Sonnet, Azure Document Intelligence, Tesseract, Docker).
- Designed a provider-switchable LLM layer with token and cost tracking, plus domain validation rules and human review points, so output was checkable rather than trusted blindly.
WORK EXPERIENCE
WiseTech Global
Apr 2025 – Jun 2026
AI Engineer - AI/ML Group
- Architected two end-to-end LLM systems for production support — CWBot, a support chatbot, and TriageAgent, an automated ticket-triage agent — combining LangGraph orchestration, FastAPI services, Azure OpenAI, enterprise APIs, and durable PostgreSQL-backed state.
- Designed RAG workflows with dynamic question rewriting, custom retriever factories, Azure AI Search integration, and heterogeneous internal data sources to improve how users find and act on operational knowledge.
- Implemented guardrails for PII filtering and response steering, plus checkpointing and recovery patterns for reliable multi-turn product experiences.
- Instrumented the production layer around the model: request tracing, structured logging, and token/cost accounting to keep behavior and spend observable once deployed.
- Led container-first CI/CD modernization for AI services (Docker dev containers, GitHub Actions, readiness checks, environment parity), with test coverage and release-readiness gates across backend, frontend, and system suites.
Redbubble
Mar 2023 – Jan 2025
Data Scientist - Search & Recommendation Team
- Led search and recommendation enhancements with Marqo vector search and GCP Vertex AI MLOps pipelines, improving add-to-cart rate by 0.5% and CTR by 10%.
- Owned production ML workflows end to end — offline evaluation, experiment design, deployment, and post-launch metric review — communicating results and model logic to product stakeholders.
- Designed ML/data infrastructure processing 100M+ user events for feature extraction, search relevance analysis, and downstream serving workflows.
- Drove GA4 analytics migration to ensure data reliability across internal dashboard-driven A/B testing frameworks.
Redbubble
Jan 2021 – Mar 2023
Data Scientist - Content & Discovery
- Shipped production content-classification and moderation systems using language-image models, reducing intellectual-property moderation risk and improving operational throughput.
- Built image duplicate-detection pipelines and data quality/anomaly-detection workflows for safer model and pipeline operation.
- Ran content tagging, taxonomy, and SEO experiments to improve product discovery.