Enias Cailliau

Teseo

Co-founder & CTO

Steamship

Put language AI into the hands of every developer.

Founding Engineer

Litto

* Productionise AI solutions for a 24/7 SaaS platform
* Develop MLOps pipelines including data engineering, data quality, and automated training
* Motivate and cultivate a team of engineers

Radix

* Lead the design of machine learning architectures  
* Supervised a successful master dissertation 
* Coached interns to become excellent machine learning engineers

Technical Lead in Machine Learning

* Develop and deploy end-to-end Machine Learning models to solve customer problems.
* Natural Language Processing - Knowledge extraction, Semantic search, Recommender engines.
* Big Data Engineering - Data Pipelines, ETL, Data Governance
* Machine Learning Operations - Building Pipelines and Maintenance.

Machine Learning Engineer

I help developers & entrepreneurs build with GPT | Founding Engineer at Steamship

Turned down Facebook to build winning startups. 

- Build what customers want.

The power of compounding.

NVIDIA

Product Research Engineer, Deep Learning

AI Makerspace

GitHub Org: https://github.com/AI-Maker-Space

FourthBrain

Instructor and Curriculum Developer

Founding Machine Learning Engineer

Explorer Hop

Data Scientist

Turtle.AI

Worked on a team of 3 to build an end-to-end ZSL (zero-shot-learning) model for setting and detecting custom wake words!

"Turtle.AI" Wake Word Project

I am obsessed with Machine Learning, Generative AI, LLMs, and more — and I love sharing knowledge as much as I love learning!

Chris "The Wiz 🪄" Alexiuk

Training a model family usually involves a massive trade-off: do you burn the compute to train Small, Medium, and Large variants separately, or do you settle for just one size?

The chefs at the NVIDIA Research kitchen just cooked up a fascinating solution to this bottleneck.

It’s called Nemotron Elastic, and it introduces a "Many-in-One" framework for training reasoning LLMs. Instead of training three separate models, the team trained a single 12B parent model that contains fully functional 9B and 6B submodels nested inside it.

The efficiency stats on this are pretty dope:

- 360x cost reduction compared to training a model family from scratch.  
- 7x cheaper than current SoTA compression techniques.  
- Zero-Shot Slicing: You can extract the smaller models instantly for deployment without any additional fine-tuning.
  
What’s really cool here is that they applied this to hybrid Mamba-Attention architectures. They solved the challenge of keeping the long-context capabilities required for reasoning tasks, even while compressing the model.  

Huge congrats to the research team for another banger, Ali Taghibakhshi and team killin' it as usual.

📄 Read the full paper here: ​https://lnkd.in/gGB8-_kW

AI @ NVIDIA - 🏗️ Build | 🚢 Ship | 🚀 Share

Just a reminder that NVIDIA has an open model for just about any task you could imagine.

Not just models, either. Datasets, code, technical reports, and more.

I've never been part of an organization that ships as much as the Team Green researchers and engineers.

https://lnkd.in/g2xbMXmB

Open article: nvidia (NVIDIA) by huggingface.co

"How do I get hired at XYZ org?"

There's 3 things I would prioritize above all else if you're looking to work in the AI space:

1. Work on your communication, no matter how good you think you are.

I learn something knew when it comes to communicating every single day. Either how to communicate with a new collaborator, or how to communicate my ideas better, or how to communicate more clearly, you get the point.

2. Please, for the love of God, learn your "handshakes".

Code interview? That's a handshake - if you know the handshake, you're more likely to get it. Random questions you feel are unrelated to the job? Another handshake, they show you can learn what you're expected to learn quickly, and thoroughly, which is what you'll need to do on the job. Behavioural interview? Believe it or not, that's a handshake. Don't put on an act, be who you'll be day in and day out, there's a company that is aligned with your work-style and vibe.

3. Understand the tech stack, and know how it fits into the ecosystem.

If you're applying to say, an AI software role at NVIDIA, and you don't know what Nemo is - it's just so much harder to explain and convince the hiring manager how you fit in, and how you're uniquely equipped to help. If you know the stack, and are passionate about some piece of it, you have a way easier time convincing someone that you belong doing that thing.

It's tough out there, with a load of talent, and AI tools complicating everything - but I truly believe that these things can help you stand out, and help you demonstrate why you're the right fit for whatever role you're after.

I know these things worked for me, and I hope they work for you as well. Stay sane out there!

Motivation for this post: I've gotten an influx of people asking me for a referral to NVIDIA recently, and while I don't refer people unless I'm familiar with their work - I do want to ensure that I give something of value to people who want to work at a tech company, and aren't sure where to get started or how to get noticed.

Google DeepMind  released Gemini 3 today, and I gotta say - I feel about this model the same way I feel about every Gemini model: "mid" (except on multimodal tasks, where it really shines bright - especially this time around.

The benchmarks are great, the hype train has definitely left the station and if chuggin' along full steam ahead - but to actually *use* the model leaves me a bit...whelmed.

I've tested it on a number of tasks and apps that I like to run new models through, and honestly it's just kind of another frontier model.

Also, it made this cute SVG of an otter riding a surfboard - and officially made Simon Willison bump the difficulty of his "pelican on a bicycle" test - so, it can't be that bad!

Happy Claude-mas to all that celebrate! Anthropic has done it again, and released yet another model that feels great to use - and crushes code tasks.

Opus 4.5 shows that we've nearly saturated SWE-bench verified (including in multilingual coding), and it's safer than ever. 

They've also added the "reasoning effort" feature we're seeing everywhere else (did Nemotron start the trend? No, but it was one of the first!) - letting you choose (through the API) if you want big think or small think!

Additionally, they did some updates to their apps - classic stuff. 

Overall - this model *feels* great to use, it is great at coding tasks, and it's not too slow for the performance. 

I'm happy to finally have a new daily driver model.

Today was a big day! Ai2 released the newest OLMo model - which is absolutely openmaxxed. OLMo 3, and the accompanying.... everything, is well worth digging through. There are a lot of great tips for all stages of training, data curation, how to think about architecture, and more.

As well, Google DeepMind dropped Nano Banana Pro, which is basically just "in every way better" Nano Banana.

It's been a busy week, and the space shows no signs of slowing down.

This is awesome!

If you want a similar, but extended version of this with a tutorial - check out this blog we wrote:

https://lnkd.in/gdhPJhAx

It's everything you need to know to get cookin' with gas!

AI Developer Experience at Google DeepMind 🔵 prev: Tech Lead at Hugging Face, AWS ML Hero 🤗 Sharing my own views and AI News

Here is the full Python code to build an simple CLI AI Agent from scratch using Gemini 3 Pro.

Build your own: https://lnkd.in/ewKj9uzp

taplio

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Enias Cailliau

Chris "The Wiz 🪄" Alexiuk

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taplio

The Battle

Enias Cailliau

Chris "The Wiz 🪄" Alexiuk

Followers

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Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Chris "The Wiz 🪄" Alexiuk's LinkedIn Performance

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