I’ve been working in AI for close to a decade, starting well before today’s LLM-driven wave.

My work is grounded in understanding AI across layers — from hardware, AI models, and data to system design, reliability, and scale. I approach AI as an end-to-end engineering problem: not just modeling, but how models interact with data, infrastructure, and real-world constraints.

Alongside this, I write The AI Merge, a technical newsletter on AI foundations and systems engineering, focused on clear thinking and practical system design rather than hype.

I’m currently building an AI Systems course, centered on end-to-end system design, for engineers who want a deeper, systems-first understanding of modern AI.

🗞️ Read The AI Merge: https://read.theaimerge.com

Alex Razvant

Read more code.

And fewer articles, tutorials, or surface-level books.

If you’re already a Software Engineer or AI / ML Engineer, one of the fastest ways to level up right now is to: 

Study how big AI teams write and ship code - in public. 

Many top companies and major contributors to the AI space have been open-sourcing their internal frameworks, kernels, or infrastructure libraries. 

You might feel overwhelmed by the rapid pace at which AI is going.
But this is a massive learning opportunity if you know where to look.

Learning doesn’t just happen from papers and tutorials, especially if you already build software.

It happens when you:
- Read real production code
- Study PRs or commit history
- See how teams structure repos, APIs, configs, tests

Check what gets reviewed, what gets rejected, and why.
This kind of learning will upgrade your engineering intuition. 

Here are some excellent codebases to learn from.

✅ From DeepSeek:
1. FlashMLA  - A library of high-performance attention kernels.
Link: https://buff.ly/XbFnPA7 

2. DeepEP - A Communication Library for high-throughput MoE workloads.
Link: https://buff.ly/3qxMc3S

3. 3FS - A Distributed Filesystem for AI Workloads, for AI training and inference.
Link: https://buff.ly/ovfW47A

✅ From NVIDIA:
1. Dynamo - A datacenter-scale Inference Serving Framework for GenAI workloads.
Link: https://buff.ly/WyAMzBz 

2. NeMo  -  A Scalable Generative AI Framework, for training and deploying LLMs and VLMs.
Link: https://buff.ly/L9KTuJp 

✅ From vLLM:
1. The vLLM Library  - One of the most popular LLM inference engines, designed for scale.
Link: https://buff.ly/GLCfoIM

✅ From SGLang:
1. The SGLang Engine - A high-performance serving engine for LLMs & multimodal models.
Link: https://buff.ly/m4HhsWQ 

✅ From llama.cpp
1. The llama.cpp Engine - A minimal C/C++ inference engine for LLMs with broad hardware support and quantization options.
Link: https://buff.ly/u9yaXwp

📌 Don't study everything, you won't be able to.

Instead, pay attention to:
⇾ Code structure & abstractions
How are core functionalities separated (core logic vs hardware-specific optimizations)?

⇾  PRs & commit history
What context do authors provide? How do reviewers push back?

⇾  Interfaces & public APIs
How are public APIs designed?

⇾  Tests & benchmarks
How do the test suites look? What CI pipelines do they have, profiling? 

Don’t just use these libraries. 
Read them.

That will build your intuition and help you get better at your craft, either as a curious learner or an engineer as part of a larger team at your job.

---
I distill my learnings with my audience on The AI Merge.
Check it here (subscribe if interested) https://buff.ly/2rwqlNL

Senior AI Engineer | Writing The AI Merge Newsletter | Helping Engineers Build AI Beyond Demos

Principal Data Scientist with over 8 years of experience in the full lifecycle of Machine Learning solutions, including strategy development, value discovery, code implementation, deployment, and monitoring. 

My expertise in ML and Optimization enables the creation of robust, accurate solutions, driving $3-10M in additional annual revenue per project.

As a Data Science Manager, I have 4 years of experience in leading, hiring, and managing data science teams. I am capable of organizing team work under harsh deadlines and complex project scopes. 

𝐈 𝐚𝐥𝐬𝐨 𝐞𝐧𝐣𝐨𝐲 𝐡𝐞𝐥𝐩𝐢𝐧𝐠 𝐨𝐭𝐡𝐞𝐫 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥𝐬 𝐠𝐫𝐨𝐰 𝐢𝐧 𝐭𝐡𝐞𝐢𝐫 𝐜𝐚𝐫𝐞𝐞𝐫 𝐛𝐲:
- Upgrading their CVs/LinkedIn
- Providing 1:1 Career Mentoring
- Sharing Free Practical ML Materials to support their technical growth

𝐌𝐲 𝐚𝐜𝐡𝐢𝐞𝐯𝐞𝐦𝐞𝐧𝐭𝐬 𝐨𝐯𝐞𝐫 𝐭𝐡𝐞 𝐩𝐚𝐬𝐭 𝐬𝐞𝐯𝐞𝐫𝐚𝐥 𝐲𝐞𝐚𝐫𝐬:

✅ Increased by $60M+ customer revenue by delivering 4 real-time end-to-end ML solutions that optimized Metals and Mining assets. During the projects, successfully managing the team of 7 Data Scientists. The solutions showed 2.5%+ increase of plants throughput and stabilized by 25%+ of the product variability.

✅ Identified potential $50M customer revenue improvement by completing 5 bottleneck analysis projects for Tier-1 metals and mining companies, resulting in new projects with them.

✅ Increased by 1.2% oil and gas production for 2 clients by leading the from-scratch development of the "AI Oil Optimization Assistant" solution. This is an ML software for accurate prediction and optimization of oil and gas production in real-time by using advanced ML and Optimization algorithms.

✅ Grew and structured a DS team from 5 to 20 data scientists who formed 4 internal teams responsible for specific solution directions.

► Tech Skills: Machine Learning | Data Science | AI | Data Analytics | Predictive Analytics | MLOps | Time Series | Anomaly Detection | Deployment  | Optimization | Git | Kedro | MLFlow | Scikit-Learn | XGBoost | Numpy | Pandas | SHAP |  Scipy | Docker

► Soft Skills: Team Management | Leadership | Empathy | Project Management | Presentation Skills | Problem Solving | Stakeholder Management

Timur Bikmukhametov

You know who is trying to make you believe that AI will write 90% of the code by 2027?



It’s CEOs of these AI companies.

Not TOP researchers.

Not the ones who ship production every day.

It’s the ones who make billions from the air out of it.

And yet, people believe it.

Timur Bikmukhametov, PhD

Helping grow ML skills & careers in my ML Academy | World Top 3 ML LinkedIn Voice (Favikon)

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