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

Helping Techies Build Personal Brands for Career & Business Growth | AI-Assisted LinkedIn Strategist to CTOs, Directors, and Tech Founders & Coaches | 100K+ Insta | Mentored 1K+ Techies | Engineering Manager @ Jio

I got promoted to Engineering Manager in 6 months of joining.

Not because I was the best engineer. But because I understood something most people miss:
Performance gets you in the room. Positioning gets you promoted.

After 9+ years in tech at companies like Jio and Byju's, I saw brilliant Directors getting passed over for VP roles, CTOs delivering results but invisible to boards, and Founders with great products struggling to raise capital.

𝙏𝙝𝙚 𝙜𝙖𝙥? 𝙎𝙩𝙧𝙖𝙩𝙚𝙜𝙞𝙘 𝙫𝙞𝙨𝙞𝙗𝙞𝙡𝙞𝙩𝙮.
So I applied positioning principles to build my LinkedIn presence to 300K+ followers. Then CTOs, VPs, Directors, and Founders started asking: "Can you help me do the same?"

That's when I quit my EM role and built a personal branding agency specifically for tech executives.

𝙒𝙝𝙖𝙩 𝙄 𝘿𝙤?
I work with tech leaders including CTOs, VPs, Directors, Founders, AI Strategists, and Senior Tech Consultants.

Past clients have landed:
→ VP and C-suite promotions
→ Board positions  
→ Advisory roles
→ Speaking opportunities at major conferences

I don't make you an influencer. I position you as THE authority in your domain.

𝙈𝙮 𝙖𝙥𝙥𝙧𝙤𝙖𝙘𝙝: Your expertise + Our AI-powered systems + Your voice
𝒀𝒐𝒖 𝒔𝒑𝒆𝒏𝒅: 30 minutes per week
𝙒𝙚 𝙝𝙖𝙣𝙙𝙡𝙚: Everything else (strategy, content, engagement)

Because at your level, your time is worth ₹30K-50K+ per day. Spending 10 hours per week on content = ₹3-5L+ monthly opportunity cost.

That's why we built a 𝙙𝙤𝙣𝙚-𝙛𝙤𝙧-𝙮𝙤𝙪 model for executives who value results over DIY learning.

𝙒𝙝𝙤 𝙄 𝙒𝙤𝙧𝙠 𝙒𝙞𝙩𝙝?

→ CTOs and VPs positioning for board opportunities
→ Directors building visibility for VP roles
→ Founders using personal brands
→ AI Strategists establishing thought leadership
→ Tech Consultants & Coaches scaling influence

If you're leading teams, making strategic decisions, let's talk.

𝙈𝙮 𝙋𝙝𝙞𝙡𝙤𝙨𝙤𝙥𝙝𝙮?
Speak like a leader. Work as an entrepreneur. Sell like Apple.

Most tech executives don't need more skills. They need better positioning.

You're already brilliant. The problem isn't competence, it's VISIBILITY.

And in 2025, visibility isn't optional. It's career infrastructure.

Let's Connect?

If you're a tech executive who has deep expertise but limited visibility, let's talk.

📩 Book Your Brand Strategy Call on topmate
💼 DM me "POSITION" for a 30-min positioning call
🌐 Follow Journey: @careerwithhina 
✉️ Business: careerwithhina@gmail.com

Recognition: LinkedIn Community Top Voice | Spoke at IIT Delhi | Featured by Indian Express, Byju's, The Voice of Women, Strong Women in IT 2025

Hina Arora

Tech hierarchy is no longer about how many engineers you manage.

It's about how well you position yourself in the AI-first era.

What I'm seeing with the CTOs and Directors I work with:

𝐓𝐡𝐞 𝐨𝐧𝐞𝐬 𝐰𝐡𝐨 𝐠𝐞𝐭 𝐩𝐫𝐨𝐦𝐨𝐭𝐞𝐝:
→ Don't just implement AI
→ Communicate the AI strategy clearly
→ Position themselves as "AI transformation leaders"
→ Build visibility around their AI initiatives

𝐓𝐡𝐞 𝐨𝐧𝐞𝐬 𝐰𝐡𝐨 𝐠𝐞𝐭 𝐬𝐭𝐮𝐜𝐤:
→ Do great technical work
→ Don't talk about it
→ Remain invisible to leadership
→ Get overlooked when opportunities come

Here's what changed for one of my clients:

Before: Director of Engineering, brilliant AI implementer, zero visibility
After: "AI Strategy Leader" on LinkedIn, speaking at conferences, offered VP role

What changed? Not his skills.
His positioning.

The future belongs to leaders who can:
→ Design systems (technical skill)
→ Communicate strategy (leadership skill)
→ Build their brand (visibility skill)

Most tech leaders have 1 or 2.
The ones who advance have all 3.

Which one are you missing?

[DM me "POSITION" if you're ready to position yourself as an AI leader]

Google just quietly launched something powerful and most people missed it.

A free AI learning platform by Google.

Link is here 👉 https://lnkd.in/gwS9gdEP

Not because it’s another certificate.
But because of how it’s built.

You get learning paths from beginner to advanced.
AI, data, cloud, productivity.
Hands-on learning, not surface level theory.
Career focused tracks, not random topics.

And the biggest part?
These courses are built by the same teams who work on Gemini and real production AI systems at Google.

This is not “learn AI in 2 hours” content.
This is structured, practical, and job relevant.

Also, it’s completely free.

If you are a student, a working professional, or someone trying to stay relevant in an AI-first world, this is worth your time.

Most people will ignore it.
The ones who don’t will quietly compound their skills.

Someone asked me this recently:
“I’ve been preparing for System Design interviews for the last 3–4 months.
Now everyone is talking about AI.
Is system design even relevant anymore?”

Short answer: 𝐘𝐞𝐬. 𝐌𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐞𝐯𝐞𝐫.

Here’s the part most people are missing.

AI didn’t replace system design.
It made bad system design very expensive.

𝐓𝐨𝐝𝐚𝐲’𝐬 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐚𝐫𝐞:
* AI-heavy
* Data-heavy
* Cost-sensitive
* Latency-sensitive
* Privacy and compliance-driven

𝐀𝐥𝐥 𝐨𝐟 𝐭𝐡𝐚𝐭 𝐬𝐭𝐢𝐥𝐥 𝐬𝐢𝐭𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐟𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬:
* How you design APIs
* How you handle scale
* How you manage failures
* How data flows
* How components talk to each other

𝐀𝐈 𝐦𝐨𝐝𝐞𝐥𝐬 𝐝𝐨𝐧’𝐭 𝐦𝐚𝐠𝐢𝐜𝐚𝐥𝐥𝐲 𝐟𝐢𝐱:
* Poor architecture
* Tight coupling
* No observability
* Weak trade-offs

𝐈𝐧 𝐟𝐚𝐜𝐭, 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐧𝐨𝐰 𝐞𝐱𝐩𝐞𝐜𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐭𝐨 𝐚𝐧𝐬𝐰𝐞𝐫:
* Where does AI fit in the system?
* What should be async vs sync?
* How do we control costs at scale?
* What happens when the model fails?
* How do we design for fallback and safety?

That’s system design with context, not without relevance.

𝐈𝐟 𝐲𝐨𝐮’𝐯𝐞 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐬𝐩𝐞𝐧𝐭 𝐦𝐨𝐧𝐭𝐡𝐬 𝐨𝐧 𝐬𝐲𝐬𝐭𝐞𝐦 𝐝𝐞𝐬𝐢𝐠𝐧:
You didn’t waste time.
You built the foundation.

Now the smart move is simple:
Layer AI on top of system design. Not instead of it.

---

𝐓𝐡𝐢𝐬 𝐢𝐬 𝐭𝐡𝐞 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐈 𝐬𝐭𝐢𝐥𝐥 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝 𝐟𝐨𝐫 𝐬𝐲𝐬𝐭𝐞𝐦 𝐝𝐞𝐬𝐢𝐠𝐧 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬:
* Algorithm: Speeds and secures data handling
* Database Selection: Chooses the right storage model
* Scalability Techniques: Handles growth and performance
* Networking: Distributes and balances traffic
* Protocols & Queues: Manages async communication and health
* Modern Architecture: Containers, serverless, deployment patterns
* Communication: Real-time and request-based interactions
* Geo-Location & Data Hashing: Spatial data and distribution
* API & Microservices: Service boundaries and integrations

AI systems still sit on top of this exact foundation.

More than half of today’s tech professionals feel their skills are becoming outdated faster than ever.

LinkedIn’s Workforce Report and multiple industry surveys point to the same truth, the tech skill cycle is shrinking.

And honestly… you can see it everywhere.

In 2020, knowing Python, SQL, or web dev was enough to feel “future-ready.”

By 2025, engineers are expected to navigate LLMs, APIs, cloud workflows, and AI orchestration.

By 2030, we’re talking about autonomous systems, AgentOps, and AI-driven workflows running at scale.

𝐓𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦?
Most professionals are still preparing for roles that existed five years ago, not the roles emerging right now.

This shift is not just about learning new tools.
It’s about understanding how careers themselves are evolving:

* Roles are moving from coding to orchestration
* Skills are moving from basics to AI + cloud hybrid stacks
* Tools are shifting from local development to intelligent systems
* Job trends are driven by automation, agents, and safety governance

𝐓𝐡𝐞 𝐭𝐢𝐦𝐞𝐥𝐢𝐧𝐞 𝐬𝐚𝐲𝐬 𝐢𝐭 𝐜𝐥𝐞𝐚𝐫𝐥𝐲:
2020 → Build applications
2025 → Automate workflows
2030 → Design systems that run themselves

If your career growth feels stuck, it’s not your capability, it’s the landscape moving faster than your strategy.

𝐓𝐡𝐞 𝐠𝐨𝐨𝐝 𝐧𝐞𝐰𝐬?
You don’t need to learn everything.
You just need to align your skills with where the industry is actually heading.

♻️ Repost this to help your network get started
➕ Follow Hina for more

How to master AI prompts to create tech-executive-level content.

Here are 7 frameworks that work with any AI (save this):

1. A.P.E (Action-Purpose-Expectation)

A | Action: Define the job or activity
P | Purpose: Discuss the goal
E | Expectation: State the desired outcome

Ex:
```
Action: Rewrite my LinkedIn headline
Purpose: Position me for CAIO role, not Senior Engineer
Expectation: Show business outcomes and scale, not technical skills. 220 characters max.
```

2. T.A.G (Task-Action-Goal)

T | Task: Define the task
A | Action: Describe the steps
G | Goal: Explain the end goal

Ex:
```
Task: Create LinkedIn post about our AI infrastructure decision
Action: Explain technical choice with business context
Goal: Position me as strategic decision-maker, not implementer
```

3. E.R.A (Expectation-Role-Action)

E | Expectation: Describe the desired result
R | Role: Specify AI's role
A | Action: Specify needed actions

Ex:
```
Expectation: Executive-level About section
Role: You're a personal branding strategist
Action: Rewrite focusing on outcomes, not expertise
```

4. R.A.C.E (Role-Action-Context-Expectation)

R | Role: Specify AI's role
A | Action: Detail the necessary action
C | Context: Provide situational details
E | Expectation: Describe the expected outcome

Ex:
```
Role: LinkedIn positioning expert for tech leaders
Action: Audit this profile for executive positioning
Context: Client is Director targeting VP Engineering
Expectation: Identify gaps, suggest 3 priority fixes
```

5. R.I.S.E (Request-Input-Scenario-Expectation)

R | Request: Specify what you need
I | Input: Provide necessary information
S | Scenario: Detail the situation
E | Expectation: Describe the result

Ex:
```
Request: Generate 10 LinkedIn post hooks
Input: Client built RAG system, saved $500K
Scenario: Positioning for CAIO role, audience is CTOs
Expectation: Hooks focus on decisions, not implementation
```

6. C.A.R.E (Context-Action-Result-Example)

C | Context: Set the stage
A | Action: Describe the task
R | Result: Describe the outcome
E | Ex: Give an illustration

Ex:
```
Context: Principal Engineer wants CTO positioning
Action: Rewrite Experience section bullets
Result: Show leadership impact, not just technical work
Ex: Change "Built ML models" to "Led ML infrastructure serving 10M users, reduced costs 40%"
```

7. C.O.A.S.T (Context-Objective-Actions-Scenario-Task)

C | Context: Set the stage
O | Objective: Describe the goal
A | Actions: Explain needed steps
S | Scenario: Describe the situation
T | Task: Outline the task

Ex:
```
Context: AI leader needs strategic LinkedIn presence
Objective: Position for board-level opportunities
Actions: Create content calendar focused on AI strategy
Scenario: Currently posts technical tutorials
Task: Generate 12 strategic content ideas
```

[If you're a tech executive tired of being the best-kept secret in your industry, DM me "POSITION”]

3 mistakes that cost you the Tech Leadership role  worth 80L CTC (even if you're qualified)

Most Directors/VPs targeting executive roles focus on the wrong things.

They perfect their resume.
They list every achievement.
They optimize every bullet point.

Then wonder why they're not getting CTO/VP/C-suite offers.

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐭𝐡𝐞𝐲 𝐦𝐢𝐬𝐬:

Tech executives aren't hired like managers.

Boards don't read 50 resumes and pick the best one.

They Google 3 names their network recommended and pick the most visible leader.

Your resume gets you considered.
Your positioning gets you chosen.

---

Here are the 3 mistakes that kill your chances:

𝐌𝐢𝐬𝐭𝐚𝐤𝐞 #𝟏: 𝐘𝐨𝐮 𝐬𝐨𝐮𝐧𝐝 𝐥𝐢𝐤𝐞 𝐚𝐧 𝐨𝐩𝐞𝐫𝐚𝐭𝐨𝐫, 𝐧𝐨𝐭 𝐚 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐬𝐭:

❌ Your resume says:
"Led engineering team of 50+ engineers"
"Shipped 15 products across 3 platforms"

✅ Executives say:
"Built technical strategy that enabled 5x revenue growth"
"Reduced infrastructure costs 60% while scaling 10x"

One shows you manage technology.
The other shows you leverage technology for business outcomes.

Boards hire the second person.

---

𝐌𝐢𝐬𝐭𝐚𝐤𝐞 #𝟐: 𝐘𝐨𝐮𝐫 𝐫𝐞𝐬𝐮𝐦𝐞 𝐢𝐬 𝐩𝐞𝐫𝐟𝐞𝐜𝐭, 𝐛𝐮𝐭 𝐲𝐨𝐮𝐫 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 𝐢𝐬 𝐞𝐦𝐩𝐭𝐲:

Here's what actually happens in executive hiring:

→ Board member hears your name
→ They Google you
→ Find your LinkedIn: Last post 8 months ago, generic headline, no insights
→ Decision made right there

"They're not visible enough for executive level."

---

𝐌𝐢𝐬𝐭𝐚𝐤𝐞 #𝟑: 𝐘𝐨𝐮'𝐫𝐞 𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧𝐞𝐝 𝐚𝐬 "𝐌𝐚𝐧𝐚𝐠𝐞𝐫" 𝐧𝐨𝐭 "𝐋𝐞𝐚𝐝𝐞𝐫":

Your resume says:
- "Scaled teams"
- "Drove technical excellence"
- "Improved velocity"

That's manager language. Not executive language.

Executives speak in:
- Business outcomes (revenue, growth, market advantage)
- Strategic vision (where technology enables opportunity)
- Board-level communication (risk, investment, ROI)
---

The brutal truth:

90% of qualified Directors/VPs never reach C-suite.
Not because they lack experience.
Because they're positioned wrong.
---

Here's what hiring committees actually evaluate:
✅ Do they have strategic vision? (Check LinkedIn)
✅ Can they communicate to non-technical audiences? (Check their content)
✅ Are they recognized in the industry? (Check engagement)

→ THEN they look at your resume.

Your resume proves you can do the job.
Your positioning proves you already think like an executive.

---

If you're qualified for executive roles but not getting there, fixing your resume won't help.

You need to fix your positioning.

[If you're a Director/VP ready to position for executive roles, DM me 'POSITION']

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