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Thomas To
Mid-level Full-Stack Engineer specializing in AI/ML data platforms for biotech and FinTech
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About
AI/ML full-stack practitioner in a small-scale manufacturing/lab operations environment who deployed a production ML system to improve blood cell order fulfillment by predicting yield/success from donor characteristics. Experienced building custom multi-agent orchestration (Python, LangChain/LangGraph, MCP) and balancing reliability, data quality constraints, and token/ROI economics while communicating tradeoffs to VP-level business stakeholders.
Experience
Education
Key Strengths
- Built and deployed an ML model to predict cell yield/success probability using donor characteristics
- Pragmatic production approach under data constraints (trained on validated subset; planned retraining as data grows)
- Strong understanding of real-world data issues (fragmented, unstructured, siloed lab/business data) and how to mitigate via stakeholder alignment
- Custom multi-agent orchestration capability (Python + LangChain/LangGraph + MCP servers) beyond low/no-code tools
- Cost/ROI-driven decision-making for agentic automation (token costs, CAPEX/OPEX vs manual ops at low volume)
- Reliability mindset for AI agents (HITL for sensitive data, integration tests, RAG to reduce hallucinations)
- Effective technical liaison to senior non-technical stakeholders (VP Finance, VP Business Ops) for infrastructure/process improvements
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