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

Andela

Key accomplishments include:
⮚ Architected robust ML and DL data pipelines for the insurance and real estate sectors, enhancing data processing efficiency by 40% through optimized workflows tailored to industry requirements. 
⮚ Developed machine learning solutions that improved predictive accuracy in insurance underwriting by 30%, directly impacting risk assessment and premium pricing strategies. 
⮚ Deployed scalable Python-based pipelines using GitHub actions with CI/CD practices, reducing deployment time by 50% while ensuring high code quality through rigorous reviews and testing processes. 
⮚ Integrated a multi-sensor labeling platform (Segments.ai) & SuperAnnotate | AI Data Platform for LLM, CV, and NLP to streamline data collection processes, resulting in a 25% reduction in manual data entry errors across projects. 
⮚ Managed AWS services including Apache Airflow, Lambda, Step Functions, CI/CD Pipeline - AWS CodePipeline & Continuous Integration Service - AWS CodeBuild , AWS CloudFormation and DynamoDB to automate workflows productively, leading to a significant decrease in operational downtime during deployment phases.

Lead Data Science Engineer | Machine Learning, Data Pipeline Engineering, Big Data Analytics

Spotr

Senior Data Engineer

Adventus.io

Senior Data Science Engineer

DATAMTX LLC

Lead Data & BI Engineer

DigitalX (Pvt) Ltd  

Cloud Solutions

Big Data Team Lead

IQVIA

Data Science & Big Data application development- Distributed big data applications development using Apache Spark & Hadoop echo systems ( Streaming/Map-Reduce/Apache Flume/Apache Storm/Apache Kafka/Apache Solr/Apache Ignite ).NLP & Machine learning based distributed application development , Machine Learning & AI Based Algorithms development for Social Media (Facebook,Twitter,YouTube) Analytics..

Senior Engineer - Data Science

Embla Software Innovation (Pvt) Ltd

As a Senior software engineer I'm directly involving with clients to provide SCRUM based software solutions.

Senior Software Engineer

CMS LK

Software requirement analysis & design & development. MVC based large scale system design & development.

Software Engineer

CN Global Pty Ltd

Web based software system analysis & design & development.

The power of data to tell stories and drive decisions has always fascinated me. My journey into the world of AI and machine learning began when I realized how much untapped potential lay within complex datasets. Over the years, I've had the pleasure of working with talented teams across various industries from leading data science initiatives at IMS Health to architecting big data & machine learning pipelines for the real state & insurance sector at Spotr.ai . Each experience has deepened my understanding of how to leverage technology effectively.

As a Senior Data Engineer at IQVIA, I directly contributed to impactful projects that utilized big data technologies like Apache Spark and Hadoop. I remember one project where we improved processing speed by 40%, allowing our clients to access insights faster than ever before. It’s moments like these that fuel my passion for what I do solving real-world problems through innovative solutions.

I thrive on challenges that push me out of my comfort zone. This was evident during my time at Datamtx LLC, where I led a team in developing distributed applications using advanced AI/ML techniques. Not only did we meet our objectives ahead of schedule but we also received commendations from clients for our efficiency, a true team effort that reinforced my belief in collaboration.

Outside of work hours, I'm always looking to expand my knowledge base. Whether it's diving into new programming languages or exploring emerging tech trends in AI/ML, I'm committed to lifelong learning. I often find myself sharing insights with fellow developers through online forums or local meetups which keeps me connected to this vibrant community.

If you're interested in discussing innovative ideas around AI/ML applications or want to collaborate on exciting projects in the tech space. Feel free to connect via LinkedIn or contact me directly at sunone5@gmail.com. I'm always open to connecting with like-minded professionals who share a passion for technology.

Skills: Python | C/C++ | Deep Learning | Machine Learning | Big Data Technologies | Data Engineering | AWS | Apache Spark | PySpark | Kafka | CI/CD | LLMs | RAG | LangChain | MLOps | AI Agents

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