Scarlett Attensil is a Senior Developer Educator and AI engineer with five years of experience applying generative models, evaluation frameworks, and production AI practices to real-world products. Based in San Francisco, she translates advanced agentic systems and LLM evaluation methods into practical developer guidance, educational frameworks, and hands-on workshops that help teams measure, test, and optimize AI deployments. Her background spans ML roles across fintech and tech—building multimodal models, fine-tuning, RAG systems, and sentiment/semantic search—combined with deep analytics experience from firms like Meta, Adobe, and Atlassian. At LaunchDarkly she focuses on AI configuration and deployment controls to give operators fine-grained flexibility; previously she consulted to startups and built generative tools for portfolio companies. Scarlett pairs rigorous statistical training (MS in Computational Statistics) with a knack for turning complex model behaviors into actionable developer tooling and documentation.
5 years of coding experience
6 years of employment as a software developer
Master of Science - MS Computational Statistics, Master of Science - MS Computational Statistics at California State University - East Bay
Bachelor of Science - BS Mathematics, Bachelor of Science - BS Mathematics at Northern Arizona University
All VEE Requirements Completed, All VEE Requirements Completed at Society of Actuaries
Multi-agent AI chatbot with dynamic configuration using LangGraph workflows, RAG search, MCP tools, and LaunchDarkly AI Configs. Control AI behavior, swap models (Claude/GPT/Mistral), enable research tools, and run A/B experiments without code deploys.
Contributions:5 reviews, 7 PRs, 117 pushes in 8 months
Contributions:12 pushes, 1 branch in 4 years 4 months
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