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Appen
CVAT
Data Annotation Tech
Labelbox
Scale AINo subject matter listed
Audio
Computer Code Programming
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VideoI have hands-on experience in AI training data workflows, including prompt evaluation, structured data labeling, and annotation pipelines for both LLM and computer vision projects. My background as a full-stack engineer has given me a deep understanding of data quality, consistency, and the importance of robust backend systems for managing large-scale annotation platforms. I am skilled in tools such as PostgreSQL, Redis, and FastAPI, and have contributed to AI annotation platforms where I improved label coverage and reduced latency through optimized data flows and caching strategies. My experience spans leading engineering teams, building analytics dashboards, and implementing end-to-end solutions that support high-quality, scalable AI data operations.
Labeled and evaluated chatbot responses for relevance, safety, and helpfulness. Performed intent classification and preference ranking to improve LLM response quality and alignment.
Annotated and reviewed over 15,000 street-scene images for object detection and lane segmentation tasks. Labeled vehicles, pedestrians, traffic signs, and road markings following strict quality guidelines. Ensured high annotation accuracy through QA checks and guideline compliance.
Bachelor of Science, Software Engineering
Bachelor of Science, Reasoning & Applied Mathematics
Lead Full-Stack Developer
Software Engineering Intern
