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

Software Engineer and AI practitioner

As a dynamic and results-driven Machine Learning Engineer, I am passionate about harnessing the power of data and cutting-edge algorithms to drive innovation. With expertise at the intersection of data analytics, software engineering, and artificial intelligence, I craft solutions that transform raw data into actionable insights.

My key strengths lie in developing and optimizing machine learning models, implementing MLOps best practices, conducting AI/DL/ML research, and deploying models in production environments while ensuring scalability, reliability, and continuous performance monitoring. I thrive in cross-functional collaborations, effectively translating business requirements into technical solutions by working closely with data scientists, engineers, and delivery teams.

Throughout my professional experience at OREL Labs, Stax Colombo, and as a research student at the University of Kelaniya, I have successfully delivered impactful projects. These include an LLM-powered conversational agent, a high-accuracy person re-identification system, an automated web scraping and topic modeling system for customer review analysis, and an automated HR performance analysis pipeline leveraging Azure cloud services.

Holding certifications in Azure AI Engineering, Google Cloud, SQL, and Python problem-solving, I am well-equipped to tackle complex challenges. I am driven by a passion for continuous learning and keeping abreast of the latest advancements in the field of AI/ML.

You can check out my portfolio projects here - https://shamalgithub.github.io/custom/

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