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

Head of AI/ML (10 years) with a strong background in Machine Learning and MLOps across various domains including information retrieval (Search, Recommendations, RAG-systems & ChatBots), NLP, and Customer Analytics. Brand new: AI Agents!

I specialize in guiding cross-functional teams of ML Engineers, Software Engineers, etc. to build scalable, event-driven ML solutions with a focus on robust MLOps practices using Python/PySpark and cloud platforms like AWS, GCP, etc. - (GCP Certified ML Engineer)

Currently, at HeyJobs, I have been fortunate to initiate and lead multiple complex ML projects (Search, RecSys, NLP and AI Agent) that that have significantly outperformed previous benchmarks and generated substantial business value. Additionally, I also designed and lead the implementation of a new MLOps and monitoring framework that has been embraced across all Data Science teams within the company.

Additionally, I love learning the latest advancements in the ML space, always exploring new ideas to push boundaries and improve outcomes. I’m also dedicated to mentoring and sharing knowledge, focused on building systems that drive innovation and business outcome.

Shantanu Ladhwe

Many wouldn't like this:
If I’m hiring for an AI Engineer…
I’m not going to ask 5 RAG flows or 10 agent flows.

Here’s what I really want to know 👇

1. Can you design a system end-to-end? from ingestion to serving? what are the bottlenecks?
2. How would you estimate costs? how to reduce it?
3. How would you reduce latency? what is a good tradeoff of latency vs quality? 
4. Do you really need self-hosted LLMs? When is it needed? 
5. How would you fine-tune language models on user behavior? which framework? what about model serving?
6. How would you construct the dataset, what about loss function? what about MLOps? 
7. Which database would you use and why? Vector DB? SQL? NoSQL?
8. What metrics would you track? How? 
9. What about system monitoring? How would you debug failure cases?
10. What about feedback loop? how would we track and evaluate?
11. How would you make the system more deterministic?
12. How would you replace embedding models and backfill embeddings without any downtime?
13. What are the fallback mechanisms?

My favourite:
1️⃣ How would you solve this problem without LLMs or vector DBs?
2️⃣ Can you solve the problem with classical IR, rules, heuristics?
3️⃣ How would you make the system more deterministic?
4️⃣ Can you explain tokenization and embeddings from scratch?
5️⃣ What happens during fine-tuning? optimizer, learning rate, layer freezing? - not just “let's use LoRA”

These are just a few top-of-mind.

The reason:
I’ve seen engineers spend months building something flashy and complex that never made it to production.
I want engineers who understand the fundamentals, not just the hype.

I don’t want a flashy agent.
I want a resilient system.

Do you agree?

PS: This can vary by role, but I’d be very cautious hiring someone with shallow foundations.

--
♻️ Repost to share with your network :)
➕ Join 37.000+ AI/ML builders here: https://lnkd.in/ds_SzEUH

Head of AI/ML - AI Agents, RAG, NLP, Recommenders, Search & MLOps

If you are into AI/ML Engineering

This one video covers the production gap that kills 90% of ML projects.

"System Design Course" by freeCodeCamp (975K+ views)
Free. 1 hour. Changes everything.

The problem with ML education: 
They teach you to build models. 
They don't teach you to serve 1000 users.

As someone building RAG & AI Agents: 
Model/LLM matters 10%. 
Infrastructure matters 90%.

🔷 What this covers:
→ Caching & CDNs: Cut RAG costs 70% 
→ Load Balancers: Handle 10x traffic without crashing
→ Database Sharding: Scale vector storage properly
→ Computer Architecture: 10x faster training and inference
→ CI/CD Pipelines: Deploy confidently

📌 Video link - https://lnkd.in/ddSYvGpc

Must watch!
---
♻ Repost if to help others!
🔔 Follow me, Shantanu for production AI/ML/MLOps & careers
➕ Join 38.000+ AI/ML builders here: https://lnkd.in/ds_SzEUH

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