WORK EXPERIENCE
WiseTech Global
Apr 2025 - Present
AI Engineer - AI/ML Group
- Architected and optimized end-to-end LLM systems for production support platforms (CWBot & TriageAgent), 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 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.
- Led container-first CI/CD modernization for AI services, establishing Docker dev containers, GitHub Actions pipelines, readiness checks, and environment parity across local and deployed workflows.
- Debugged production-like infrastructure issues across Azure Search schemas, devcontainer networking, PostgreSQL SSL, and service configuration drift.
- Maintained test coverage across backend, frontend, and system test sets, incorporating linting, type checks, quality gates, and release-readiness checks.
SGLang Framework
Feb 2025 - Present
Open Source Contributor
- Contributing core optimizations to the SGLang framework (LMSYS / UC Berkeley) to accelerate LLM inference pipelines for state-of-the-art models including DeepSeek R1, Llama 3, and Qwen.
- Working on backend runtime, distributed serving, and frontend prompt-language features to improve throughput, contextual routing, and controllability.
- Leveraging AI coding agents (Claude Code) to navigate the large codebase, draft PRs, and review diffs, demonstrating an effective human-in-the-loop open source contribution workflow.
Australia IT Group · AI Engineer Bootcamp
Feb 2026 - May 2026
Guest Instructor & Curriculum Designer — RAG, Multi-Agent Systems, Fine-tuning
- Delivered 12+ sessions on production RAG, embeddings & vector search, multi-agent systems (LangGraph, AutoGen, CrewAI), QLoRA fine-tuning, RAGAS evaluation, and applied AI (UI, PDF parsing, Text-to-SQL); all notebooks and slides committed to the cohort repo.
- Designed Dispatch.AI, a 6-week hands-on course project (13 students): students build a production AI booking assistant incrementally — Pydantic state + Redis persistence, LangGraph agents, MCP tool server, multi-agent routing, NeMo Guardrails, RAGAS evaluation, and Docker + Render capstone; delivered reference implementation, scaffold, and CI/CD grading pipeline.
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-shaped ML workflows from analysis and offline evaluation through experiment design, deployment coordination, and post-launch metric review.
- Designed ML/data infrastructure over 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.
- Optimized search relevance for long-tail queries, balancing retrieval quality, user behavior signals, and measurable business impact.
Redbubble
Aug 2022 - Mar 2023
Data Scientist - AI Renovation Team
- Applied language-image models for automated content tagging and classification across a large marketplace catalog.
- Enhanced content taxonomy using structured user engagement signals and measurable search/discovery outcomes.
- Ran experiments for new content tags and search-engine optimization, translating model output into product-facing discovery improvements.
- Developed personalization workflows that connected data science experiments with production product metrics.
Redbubble
Jan 2021 - Jul 2022
Data Scientist - Content Moderation Team
- Implemented production content-classification systems using language-image models for automated moderation.
- Developed image duplicate-detection pipelines using ML and statistical modeling techniques.
- Established data quality and anomaly-detection workflows for safer model and pipeline operation.
- Shipped classification workflows that reduced intellectual-property moderation risk and improved operational throughput.