# Vasili Onjea

**Product Engineer · San Francisco, United States**

Product Engineer with extensive experience in frontend development and AI agent orchestration. Proven track record of building scalable web applications and optimizing complex systems.

Profile: https://path.cv/vasili_onjea

*Last updated: July 2026*

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## Experience

### Product Engineer (Independent R&D) at Self-employed
*Sep 2022 – Present*

Built an AI research tool using GCP Cloud Tasks and Fastify to handle long-running LLM workflows. Developed a local RAG pipeline using Orama and LangChain for efficient document searching.

### UX Engineer at Google
*Feb 2019 – Apr 2022*

Developed and maintained web applications using Angular and Material Design. Revamped unit tests and defined CI/CD pipelines to improve code quality.

### Software Engineer (Frontend + UI) at LinkedIn
*Nov 2017 – Jan 2019*

Led front-end development for the Sponsored Messaging platform. Mentored engineers on EmberJS and modern JavaScript through pair programming and code reviews.

### UI Engineer at LinkedIn
*Apr 2015 – May 2017*

Led front-end architecture for the Guided Search feature in LinkedIn Recruiter. Integrated UI with ATS partners and revamped Recruiter InMail.

### Web Application Developer at Self-employed
*Jul 2013 – Feb 2015*

Worked as an independent freelancer developing custom Content Management Systems. Contributed to various open source projects.

### Front End Engineer at Real Magnet
*Feb 2014 – Jul 2014*

Front end engineering role focused on web application development.

### Front End Developer at Smashing Boxes
*Feb 2013 – Jul 2013*

Front end development for various client projects.

### Front End Developer at Autoshop Solutions
*Apr 2012 – Feb 2013*

Front end development for automotive industry web solutions.

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## Skills

GCP Cloud Tasks · Agentic Workflows · LLM Orchestration · React · TypeScript · Angular · Fastify · Firestore · LangChain · Orama · EmberJS · JavaScript · Figma

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## Projects

### AI Research Tool

Tool using GCP Cloud Tasks and Fastify for long-running LLM workflows.

### Local RAG Pipeline

In-memory search pipeline for 10-K filings using Orama and LangChain.

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## Links

- **LinkedIn**: https://www.linkedin.com/in/billyonjea