Wired 🧠🦠🧬

Berlin • AI systems researcher focused on production-grade ML/GenAI: evaluation-first design, reliable serving, and real-world robustness.

khaled777@dr.com • linkedin.com/in/khaled777b • github.com/wired777b

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🧠 What I’m about

I research and build AI systems that remain reliable in real environments: shifting data, messy inputs, latency constraints, and hard evaluation questions.

My north star is “measurable intelligence”: evaluation harnesses, regression gates, and observability that make model behavior auditable over time.

🤖 AI systems focus

• 🧪 Evaluation-first ML/GenAI: offline metrics + regression suites, task-specific checks, and monitoring loops.
• 🧠 RAG systems: hybrid retrieval, indexing strategies, reranking, grounding, and quality/safety guardrails.
• 🧬 Fine-tuning when justified: 7B+ class models, dataset curation, and careful error analysis (not vibes).
• 🏗️ MLOps & serving: reproducible pipelines, versioning, rollout patterns, multi-model serving.

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🧰 Core stack (production favorites)

Modeling

RAG / Retrieval

Serving / MLOps

APIs / Streaming / Data

Observability

More tools

Dev & delivery

Data

Mobile

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🧱 What I build (system-level)

• 🤖 AI systems: evaluation harnesses, model/retrieval regression gates, and monitoring that catches drift early.
• 📚 RAG platforms: hybrid retrieval, chunking/index strategies, reranking, grounding checks, and cost/latency controls.
• ⚙️ Serving & pipelines: cloud-native deployments, safe rollouts, multi-tenant patterns when needed.
• 🛰️ Data reality: streaming ingestion, replayability, idempotency, schema evolution.

🧠 Principles

• 🧪 Evaluation over opinions; metrics + datasets are the contract.
• 🔭 Observability is non-negotiable; debugging should be fast and boring.
• 🧱 Maintainability is velocity; clean boundaries beat cleverness.
• ⚖️ Reliability is designed; failure modes should be predictable.

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🧫 Current project: gut health (microbiome + nutrition)

I’m applying the same AI-systems discipline (evaluation, guardrails, and reliability) to gut-health use cases where data is noisy and “sounds plausible” is not enough.

🌍 Quick facts

• Berlin-based.
• Languages: English • French • German • Spanish
• Interests: AI systems, GenAI/RAG, MLOps, streaming platforms, computer vision, open source.

If you're building AI systems that must hold up in production — let’s connect.