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Benjamin Kozel
Mid-level Machine Learning & Software Engineer specializing in RAG systems and ML infrastructure
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About
Built and deployed an in-house RAG LLM system ("MONTY") using LLaMA 3B + FAISS to help teams quickly understand long internal/external specifications. Delivered usable production performance despite severe compute limits (single RTX 3080) by tuning retrieval/reranking and model choice, and is planning a LightRAG/knowledge-graph rewrite to improve accuracy and latency.
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Key Strengths
- Built and deployed an internal RAG assistant (MONTY) to help teams navigate long specifications
- Optimized LLM system performance under tight hardware constraints (single RTX 3080) via smaller models and retrieval/reranking tuning
- Pragmatic model/stack selection based on constraints, objectives, and target metrics
- Reliability-focused design with explicit fallback behaviors and failure-mode testing (e.g., server failure, bad sensor)
- Metrics-driven evaluation using scorecards (e.g., TTFT, perplexity, retrieval/generation metrics)
- Effective cross-functional alignment by translating model behavior into stakeholder-relevant metrics and sprint artifacts
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