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Christopher Kanten
Senior Backend Engineer specializing in Node.js, Java, and regulated SaaS platforms
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Built a production LLM-powered root cause analysis agent for supply chain alerts that helped operations managers avoid manual dashboard investigation. Demonstrates unusually strong depth in agent reliability, orchestration, and observability, with concrete production practices like hallucination blocking, shadow testing on 500 cases, and data-driven improvements that raised user agreement to 94% while cutting GPT-4 usage by 60%.
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Key Strengths
- Shipped production LLM feature end-to-end for supply chain operations
- Designed agent architecture with tool calling and LangGraph state machine
- Strong reliability engineering for agent systems
- Built hallucination controls via evidence cross-checking against tool call IDs
- Proactive failure-mode design for loops, timeouts, missing tools, and cost overruns
- Backend orchestration with FastAPI, Redis queues, and Postgres step persistence
- Thoughtful latency/cost tradeoff design with sync vs async execution paths
- Robust evaluation framework with nightly offline evals and edge-case regression suite
- Data-driven iteration improved user agreement from 70% to 94%
- Reduced unnecessary GPT-4 usage by 60%
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