Eyal Pasha
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Israel

AI/ML engineer in Israel working across Hebrew NLP, research, production systems, and international delivery.

A closer look at Israel-based AI/ML work across Hebrew AI, applied machine learning, academic research, production systems, and English-language collaboration with Europe and international teams.

By Eyal Pasha (אייל פשה) · Last updated April 9, 2026

I am based in Israel and work across AI/ML architecture, Hebrew-language AI, computer vision, academic research, production delivery, and English-language collaboration with Europe and international teams.

The work spans enterprise LegalTech AI, multi-model LLM orchestration, NLP pipelines, document intelligence, Azure-hosted infrastructure, and systems that need to hold up under real users, operational constraints, and business expectations.

On the academic side, I led WMSM, a real-time Hebrew Sign Language framework later published by IEEE. That creates a real research trail alongside the production work, tying local work in Israel to credible academic output.

Background

  • Based in Israel and comfortable leading work in English across enterprise, research, startup, and distributed international environments.
  • B.Sc. in Computer Science and MBA with interdisciplinary thesis through The College of Management Academic Studies (COLMAN) in Rishon LeZion.
  • Active in Hebrew AI, Hebrew NLP, and Hebrew Sign Language research.
  • Current role at Sensemaking Israel, building production AI systems with business relevance.

What I bring

  • Lead ML/AI Engineer with product and architecture ownership.
  • Applied machine learning developer with full-stack delivery capability.
  • Work that runs from model design through infrastructure, deployment, and commercialization.
  • Comfortable across AI strategy, product systems, hands-on implementation, and academic collaboration with distributed stakeholders.

Areas of focus

Hebrew AI Hebrew NLP Applied ML Rishon LeZion Rehovot LegalTech AI Computer Vision Production Systems AI Architecture

What stands out

Work shaped by both research and real product responsibility.

That mix is what gives the work its range.

Research

Published work

Lead author of WMSM, focused on Hebrew Sign Language recognition, sentence-level translation, and efficient real-time ML.

Production

Enterprise AI systems

Building AI systems that integrate LLM orchestration, NLP pipelines, databases, and product delivery in one stack.

Academic + business

Interdisciplinary thesis

MBA with an interdisciplinary thesis connecting Computer Science and Business Administration around deep learning commercialization.

Questions

A few things people usually want to know.

The short version of how the work fits together.

What makes this work especially tied to Israel?

Eyal Pasha (אייל פשה) builds AI systems rooted in the Israeli tech ecosystem. His Hebrew NLP work addresses challenges unique to the Hebrew language, including right-to-left processing, complex morphology, and limited training data. He studied at The College of Management Academic Studies (COLMAN) in Rishon LeZion, where he completed both a B.Sc. in Computer Science and an MBA with an interdisciplinary thesis on deep learning commercialization. His current role at Sensemaking Israel in Rehovot focuses on enterprise LegalTech AI for the Israeli legal market. His IEEE-published research on Hebrew Sign Language recognition directly serves the Israeli deaf community.

Does the work focus more on research or on production delivery?

Both tracks run in parallel and reinforce each other. The research track produced WMSM, a Hebrew Sign Language recognition framework published at IEEE FLLM 2025 in Vienna, achieving 97.29% word-level accuracy while cutting training costs by 99.93% compared to existing benchmarks. The production track includes architecting and leading a full enterprise LegalTech SaaS platform — the first AI-native operating system for law firms — with multi-model orchestration across OpenAI, Gemini, and local LLMs via vLLM. The research directly informs production decisions around model efficiency, Hebrew-specific NLP pipelines, and cost-effective deployment strategies.

What kinds of work come together here?

The work spans Hebrew NLP and natural language processing for Hebrew-language documents, LegalTech AI for enterprise law firm operations, computer vision and real-time gesture recognition for Israeli Sign Language, multi-model LLM orchestration and production AI pipeline design, full-stack product architecture on Azure with PostgreSQL, and deep learning research published through IEEE. This combination of applied machine learning, Hebrew-language AI, academic research, and commercial product delivery is what defines the scope of the work.

More to read

More on the LegalTech platform, academic background, and research.

A few deeper reads on specific parts of the work.

LegalTech AI

Hebrew NLP and LegalTech AI in production legal workflows.

A closer look at enterprise document intelligence, multi-model orchestration, and Hebrew-English AI delivery for cross-border legal workflows.

Read more

Research

WMSM research on Hebrew Sign Language recognition.

A closer look at the WMSM publication, the key results, and the international research-to-product path into Handibur.

Read more

Academic

Academic AI research in Israel, from IEEE publication to interdisciplinary thesis.

A closer look at the publication record, international conference footprint, COLMAN background, and the interdisciplinary thesis.

Read more

Contact

Open to teams, research groups, and builders in Israel.

If the work needs Hebrew-language AI depth and production experience, this is a good place to start.

Email or LinkedIn is the fastest route.

Open to conversations about AI engineering roles in Israel, applied ML leadership, Hebrew NLP, research collaboration, and startup-building work.

eyal@eyalpasha.com LinkedIn GitHub IEEE ResearchGate Scholar ORCID