The AI 1000 Elite: 20 Practitioners You Should Know

The public narrative around AI is dominated by spectacle — demos, debates, and declarations about artificial general intelligence. But the actual work of building AI? That happens quietly. It’s written in pull requests and deployment logs. It’s debated in Slack threads, not press releases. And it's executed by a specific kind of practitioner: engineers and scientists who aren’t seeking the spotlight — they’re too busy moving the field forward.

This list is about them.

The AI 1000 Elite is a curated group of twenty such builders — leaders inside the AI/ML ecosystem who are defining how intelligence gets deployed at scale. They aren’t theorizing about infrastructure. They’re shipping it. They’re the ones designing inference pipelines, scaling training workflows, and aligning model performance with business outcomes.

If the AI 1000 showed us the full ecosystem of talent behind today’s machine learning stack, this is its sharpest edge — the system-level architects you don’t see on conference stages, but whose code your products already depend on.

Let’s meet them.

Marton Trencseni — VP of Data, Majid Al Futtaim (Dubai, UAE)

With 16 years of experience across distributed systems and data engineering, Marton leads AI infrastructure at one of the largest retail groups in the Middle East. His tool stack spans Haskell to Presto, and his fingerprints are on systems used by millions.

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Jared Flatow — VP of Engineering, Halliday (Palo Alto, CA)

Jared blends cryptography, applied ML, and scalable systems design. A polymath with deep expertise in bioinformatics and distributed compute, he's currently scaling infrastructure at a fast-growing blockchain-based identity platform.

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Brian Haveri — VP of Engineering, TrainingPeaks (Denver, CO)

Brian leads productized AI and DevOps for an athletic performance SaaS, drawing from a background in Python, Laravel, and Flutter. He’s one of the few who’s scaled AI in a consumer fitness context with both agility and rigor.

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Sam Reis — VP Engineering, lemon.markets (Berlin)

Sam runs ML infrastructure for algorithmic trading systems in fintech. With roots in C++, AWS, and systems optimization, he's building the kind of low-latency pipelines that most startups only simulate in notebooks.

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Isaiah Norton — VP of Engineering, TileDB (Cambridge, MA)

Isaiah has shaped scalable biomedical and geospatial data platforms from the ground up. His deep experience in C++, distributed query engines, and SaaS productization makes him a critical node in the future of AI-ready data storage.

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Gautham Ponnuvel — Head of AI, First Digital Trust (Hong Kong)
Gautham bridges financial modeling and AI automation in the web3 custody space. He brings over a decade of experience across data modeling, secure compute, and blockchain integration.

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Aashish Gadani — CTO, Qureight (Cambridge, UK)
A former NHS medical imaging scientist turned AI leader, Aashish now applies ML to pulmonary disease modeling. He’s advancing the state of explainable medical AI in real-world diagnostics.
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Liang Xu — Director of AI, Genomics England (London, UK)
Liang is one of the unsung heroes of bioinformatics, translating ML models into NHS-validated pipelines for genome interpretation at population scale.


Janice Lin — Head of ML Infrastructure, Rocketplace (San Francisco, CA)
A former Uber ML infra engineer, Janice now leads backend intelligence for a new investment platform. Her focus: scalable feature stores, real-time inference, and low-latency decisioning.
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Arnab Das — Lead AI Scientist, DeepSource (Bangalore, India)
Arnab is reshaping code quality automation through deep learning. His work merges static analysis and transformer models, bringing intelligence to CI/CD in software development.

Siddharth Sharma — CEO & Co-Founder, MetaVoice (San Francisco, CA)
Siddharth is building voice-first LLM tools that blend speech synthesis and conversational AI. His stack spans C++, PyTorch, and deep signal processing.

Kerri Rapes — CTO, TrustBytes (Indiana, USA)

‍Kerri’s leadership bridges Web3 and AI ops, creating automated verification layers for decentralized infrastructure. She’s one of the few technical founders fluent in both Solidity and distributed AI protocols.

Phil Ferriere — Principal Engineer, Outrider (Palm Springs, CA)
Phil is a veteran of robotics and computer vision, now building autonomous yard trucks. His skillset includes Deep Learning Ops, embedded C++, and scalable perception systems.

Jeffery Rifwald — Principal Frontend Engineer, Aspira (Knoxville, TN)
Jeffery isn't just a frontend lead — he's a rare bridge between ML teams and UX teams, working at the intersection of real-time inference and user feedback loops.

Oleg Avdeëv — Co-Founder, Outerbounds (San Francisco, CA)
Oleg co-founded one of the most promising orchestration platforms for ML pipelines. He’s a key architect of Metaflow, and a rising voice in ML observability.

Patrick Lauber — Founder, getitAI (Zurich, Switzerland)
Patrick’s startup focuses on low-code AI deployment tools. With prior experience in cloud-native dev tools, he’s now building abstractions for AI that don’t sacrifice control.

Shrey Anand — Principal Data Scientist, Red Hat (Toronto, CA)
Shrey operates inside one of the world’s most foundational open-source companies, deploying scalable ML on Kubernetes and integrating model performance directly into CI workflows.

Richard Evans — VP Technology, AZX (Washington, USA)
Richard’s work spans predictive analytics, recommender systems, and dev tooling. His team focuses on embedded ML systems within retail and loyalty platforms.

Sigurd Spieckermann — Principal Scientist, Siemens (Germany)
‍Sigurd brings AI to the industrial edge — optimizing machine data pipelines, anomaly detection models, and scalable automation infrastructure for one of the world’s largest engineering firms.

Carmine Paolino — Founder & CEO, Chat with Work (Berlin)
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‍Carmine leads a conversational AI startup in the HR tech space. His vision combines natural language understanding, job matching, and voice interface design — all built on a lean tech stack optimized for experimentation.

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Why This List Matters

These twenty aren’t the loudest voices in AI. But they’re among the most consequential. They’re shipping code, not commentary. And they’re doing it inside teams where model quality, infra efficiency, and business value converge.

They represent the center of gravity for modern AI productization — from data prep to inference to observability.

If you’re a GTM leader, these are the people your buyers trust.
If you’re hiring, these are the types of builders you need.
If you’re selling AI tools, these are the ones you should be designing for.

In a world where the volume of AI noise is only increasing, precision targeting has never mattered more.
And the clearest signals don’t come from titles. They come from what’s been built — and what’s being built next.

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This isn’t a list scraped from LinkedIn. It’s curated using contact-level technographics — a methodology that traces real digital activity, not resume fluff.

Every name here is the result of verified open-source work, team-level analysis, hiring momentum, and GitHub contribution history.

If you want access to the full AI 1000 or this Elite segment — for research, recruitment, or targeting — reach out.

We’re happy to share the data. Just be ready to use it wisely.

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