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AI Researcher | Author | Founder at EmitechLogic Author of Neural Networks and Deep Learning with Python: A Practical Approach
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Assessing your cultural and operational fit
EmiTechLogic
Software Engineer
June 21, 2026 – Present
context-window-engine
June 9, 2026 – Present
Proves that larger context windows don't fix RAG on structured data — they make wrong answers harder to detect. Then solves it with a query router that prevents RAG from being used for aggregation in the first place.
View Projectrag-cost-control-layer
May 25, 2026 – Present
A pure-Python cost control layer for RAG pipelines — semantic caching, query routing, token budget enforcement, and circuit breaking in one system.
View Projectcontrol-layer
May 18, 2026 – Present
A production-grade control layer that sits between your application logic and any LLM — input validation, schema enforcement, circuit breaking, targeted retry, and audit logging in one composable pipeline.
View Projectllm-eval-layer
May 14, 2026 – Present
A lightweight LLM evaluation layer that turns subjective model outputs into deterministic decisions by scoring attribution, specificity, relevance, and context quality—designed to detect hallucinations and enable production-grade LLM reliability.
View Projecttemporal-rag
April 28, 2026 – Present
A post-retrieval temporal layer for RAG systems — validity filtering, time decay, document kind classification, and hybrid reranking in one pipeline.
View Projectpytorch-nan-detector
April 25, 2026 – Present
PyTorch NaNs are silent killers. This hook catches them at the exact layer and batch — with ~3 ms overhead vs ~7 ms for set_detect_anomaly.
View Projecthallucination-detector
April 23, 2026 – Present
Production RAG hallucination detection + self-healing pipeline — 5 checks, 3 healing strategies, zero external APIs
View Projectcontext-engine
April 10, 2026 – Present
A pure-Python context management layer for LLM systems — retrieval, re-ranking, memory decay, and token-budget enforcement in one pipeline.
View Projectreact-retry-waste-analysis
April 2, 2026 – Present
ReAct agents waste 92.6% of retries. Here's the architecture fix (error taxonomy + circuit breakers + deterministic routing) that drops waste to 0%.
View Projectneuro-symbolic-ai-fraud-pytorch
March 13, 2026 – Present
End-to-end differentiable rule learning for fraud detection. A neural network discovers its own IF-THEN rules via temperature annealing — no hand-coding required.
View ProjectCultural Fit Analysis
The candidate's projects are highly specialized in AI/ML, specifically LLM and RAG systems, which suggests a strong passion and deep interest in this domain. This specialization could be a good cultural fit for roles focused on advanced AI development. However, the lack of diversity in technologies beyond Python and the singular focus on AI/ML might indicate a narrower breadth of experience, which could impact adaptability to broader software engineering challenges or different tech stacks. The candidate's experience level is listed as 0, which might indicate a recent graduate or someone new to the industry, despite having a current 'Software Engineer' role listed.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a problem-solving mindset and an ability to identify and address complex technical challenges in AI/ML. However, without completed psychometric or English tests, it is difficult to assess communication clarity, work attitude, stress handling, or team collaboration skills. The candidate's experience level is listed as 0, which contradicts the current employment entry, making it hard to gauge operational fit accurately.