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Jamie Cook
Senior Machine Learning Engineer specializing in AI search and recommendation systems
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Built internal production LLM tools for engineering and support, including a customer-health assistant and a RAG-based incident explainer grounded in logs, metrics, and deploy data. Stands out for combining strong GenAI safety/evaluation practices with pragmatic backend engineering, delivering measurable impact like a 40% drop in data-help requests and answers in seconds instead of minutes or hours.
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Key Strengths
- Owned production LLM systems end-to-end from prototype through deployment, monitoring, and iteration
- Strong grounding and safety design for GenAI systems
- Translated research-style tool-calling into reliable production architecture
- Improved operational efficiency with measurable impact
- Reduced data help requests by 40%
- Cut answer time from minutes or hours to seconds
- Balanced latency, cost, quality, and safety in production ML systems
- Built scalable internal developer tooling with clean APIs and strong test strategy
- Improved reliability through shared validated Python libraries and contract testing
- Drove adoption of internal AI tooling to roughly 70% weekly usage
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