Samuel Queiroz
Applied Ai Engineer @Activestate
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WORK HISTORY
Applied Ai Engineer @Activestate
Vancouver, BC, CA
Designed and implemented LLM-based agentic workflows (Python, async orchestration)• Implemented evaluation framework (golden datasets, regression testing, prompt versioning)• Reduced hallucinations by 90% through guardrails + structured outputs• Instrumented latency + cost metrics; achieved near-real-time inference performance• Built human-in-the-loop feedback loop to continuously improve model outputs
EDUCATION
Quantic School of Business and Technology
Executive MBA
INSA Lyon - Institut National des Sciences Appliquées de Lyon
International Student, Computer Science
Universidade Federal do Rio Grande do Norte
Master's degree, Cloud Computing
Universidade Federal de Campina Grande
Bachelor's degree, Computer Science
SKILLS
ABOUT SAMUEL QUEIROZ
I’m an Applied AI Engineer with 12+ years building distributed systems and production ML applications, including RAG-powered products used by tens of millions of users and LLM-powered systems empowering thousands.Over the past few years, I’ve focused deeply on:• Agentic workflows with human-in-the-loop feedback• Retrieval-Augmented Generation (RAG) systems• LLM evaluation frameworks (offline + online)• Cost, latency, and reliability optimization• Guardrails and hallucination reduction in productionI enjoy building AI systems end-to-end: from experimentation to evaluation to deployment and monitoring. I care deeply about feedback loops, measurable improvements, and shipping real products to customers.Keywords: RAG pipelines (indexing strategy, chunk size experiments), vector databases (Milvus), eval metrics (precision/recall), prompt versioning approach, guardrails (regex, structured output, function calling), cost optimization techniques, agent orchestration patterns, LLMs, recommendation systems, AI safety.
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