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Timothy Yeav
Senior AI/ML Engineer specializing in Generative AI and FinTech
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About
Built end-to-end LLM/RAG systems for biological data and scientific literature analysis in a drug discovery setting, helping researchers explore disease insights and treatment hypotheses faster. Combines applied GenAI product work with strong production engineering, including monitoring, retrieval optimization, reusable Python services, and scalable deployment on AWS/Kubeflow.
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Key Strengths
- end-to-end ownership of production ML/AI systems
- LLM/RAG systems for scientific and biological research
- translating research ideas into scalable production implementations
- balancing latency, cost, quality, and safety tradeoffs
- strong production monitoring and feedback-loop design
- iterative retrieval optimization using chunking and ranking improvements
- prompt refinement based on real user behavior
- building reusable Python services and internal ML libraries
- test-driven service design with unit, integration, and regression coverage
- standardizing model serving and validation to improve engineering velocity
- domain expertise in drug discovery and genomics workflows
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