Three eval modes — output, trajectory, and side effect — and the practical pattern we use when there's no single right answer.
Hi, I’m Imran.
I build applied AI/ML systems. Currently co-founding VEZRAN — shipping Zyberpol, an agentic Security Operations Center platform. Previously Senior Data Scientist at Starbucks and FedEx.
I write about production ML, contribute to open-source AI infrastructure, and disclose security research to the projects I depend on.
- 26+
- merged OSS PRs
- 8+
- years shipping ML in production
- 1
- agentic SOC platform, in production
- 3
- companies (incl. one I co-founded)
Writing — long-form essays
all →Open source — 26 merged PRs, 4 open
all →Security research
HF Transformers path-traversal cluster
Four-site path-traversal cluster in checkpoint loading — extends the #46097 fix. Disclosed via Huntr + security@huggingface.co with proof-of-concept and remediation.
Huntr #38583 · HuggingFace
DSPy Security Bench
Reproducible security benchmark for DSPy pipelines — prompt injection, jailbreak, and adversarial-example attacks across representative DSPy programs.
immu4989/dspy-security-bench · Open source (own repo)
Notes — short-form
all →Projects
all →Zyberpol — agentic SOC platform
Co-founder and Head of AI/ML. Multi-agent autonomy on top of an existing security stack, with audit-ready evidence for every action.
Contact-center NLP at Starbucks
Transformer-based topic modeling, summarization, and semantic search over partner contact-center data.
Package-at-risk and loss-intensity models at FedEx
Production ML across the logistics network. Identified at-risk shipments before they failed; quantified customer-level loss exposure.
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Field notes on applied AI — agent eval, RAG in production, OSS finds, what the demos leave out. One essay every couple of weeks. No spam.
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Elsewhere on the web
TODO: add X, HuggingFace, Medium, HackerNoon once profiles exist