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PoHung Chen
Junior AI/ML Engineer specializing in MLOps and real-time model serving
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About
Software engineer with Amazon experience who has built LLM-powered and hybrid ML systems for ad auction/relevance at massive scale. Most notably, they described redesigning brand-query classification with a GPT-4-assisted offline cache plus fallback architecture that improved accuracy from 72% to 99%, reduced latency and costs, and was credited with an estimated $130M revenue lift.
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Key Strengths
- Built production LLM/hybrid systems for high-scale ad auction workflows
- Strong trade-off judgment across accuracy, latency, and cost
- Designed two-tier architecture with Redis cache and fallback models
- Improved classification accuracy from 72% to 99%
- Reduced latency by about 20-22%
- Cut operating costs by 60%
- Drove estimated $130M incremental revenue via higher CTR
- Maintained 100% service availability with fallback design
- Experience with evaluation loops using offline replay and online A/B testing
- Data reliability engineering with schema validation, idempotent pipelines, and DLQs
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