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Tamir ShemTov
Entry-Level Computer Vision Research Assistant specializing in medical imaging AI
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About
New grad who shipped an LLM-powered writing app (“Write-it”) to production on Azure with CI/CD (GitHub Actions + JFrog) and implemented an unconventional RAG pipeline to prevent repetitive prompts using embeddings and cosine similarity. Also participated in a Luma AI image/video generation hackathon, iterating with artist feedback and improving usability by rewriting non-technical prompts via an LLM.
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Key Strengths
- Built and deployed an LLM-powered app to production on Azure (Container Apps + Docker + Cosmos DB)
- Designed a novel RAG approach to enforce prompt diversity using embeddings + cosine similarity thresholds
- Strong prompt-engineering practices (context ordering, constraints, temperature tuning, self-validation safeguards)
- Reliability-minded AI workflow design (metrics-first, modular safeguards, fallback behaviors, monitoring for drift)
- Able to translate AI capabilities for non-technical stakeholders and incorporate user feedback (artists at hackathon)
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Experience
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Awards
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1 publication
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