Alessya V
Engineering Leader at Apple
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolAlessya V is an engineering leader and founder who has spent her career bringing production-ready AI systems and observability tooling to large enterprises. As CEO and co-founder of WhyLabs she built an AI observability platform to prevent model failures and drive measurable impact, and now leads engineering at Apple, bridging startup grit with hyperscale product discipline. Her background spans hands-on software and test engineering at Amazon through senior program leadership, giving her uncommon fluency across ML platforms, data infrastructure, and operational reliability. She combines an applied-math foundation and an MS in entrepreneurship to translate research-stage ideas into adopted, widely used tooling—an aptitude she demonstrated as CTO-in-Residence at AI2 vetting multiple AI ventures. Colleagues describe her as mission-driven and pragmatic: she focuses on building toolchains that let teams operate AI responsibly, not just experimentally.
2 years of coding experience
19 years of employment as a software developer
BS in Applied Mathematics discrete modeling combinatorics algorithm design and optimization linear programming, BS in Applied Mathematics discrete modeling combinatorics algorithm design and optimization linear programming at University of WashingtonMS in Entrepreneurship Business Management Marketing and Related Support Services, MS in Entrepreneurship Business Management Marketing and Related Support Services at UW Foster School of BusinessAAS in Software Solutions Development web applications databases networks, AAS in Software Solutions Development web applications databases networks at Clark College
English, Russian
Github Skills (15)
dataops10
logging10
prompt-injection10
data-quality10
nlg10
analytics9
data-science9
python9
data-pipeline9
machine-learning9
mlops9
nlp9
prompt-engineering9
large-language-models8
observability7
Programming languages (1)
Github contributions (2)
whylabs/langkit
Jul 2023 - Nov 2024
🔍 LangKit: An open-source toolkit for monitoring Large Language Models (LLMs). 📚 Extracts signals from prompts & responses, ensuring safety & security. 🛡️ Features include text quality, relevance metrics, & sentiment analysis. 📊 A comprehensive tool for LLM observability. 👀
Contributions:6 reviews, 11 PRs, 3 pushes in 1 year 3 months
large-language-modelsmachine-learningnlgnlpobservability
whylabs/whylogs
Sep 2023 - Dec 2024
An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collection, ensuring safety & robustness. 📈
Contributions:15 reviews, 7 PRs, 5 pushes in 1 year 3 months
pythonloggingai-pipelinesdata-qualityml-pipelines
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