These are actual high-demand next-level AI skills, not RAG:
- Data prep for instruction fine-tuning
- @UnslothAI ecosystem for fine-tuning, reasoning models, quantization, etc.
- Fine-tuning embedding models
- Backend design using FastAPI, Redis caching, queue workers, rate limiting
- Making LLM inference layer using @vllm_project
- @DeepSpeedAI for multi-GPU training
- Learn NVIDIA Triton for running any vision and object detection models along with LLMs
- Learn about Docker and CUDA setup
- Use of @huggingface ecosystem
- Distributed systems using @anyscalecompute
- Kubernetes and Terraform setup for serving your model
- Learn about @modal for low-config setup deployment
- Deploying LLM on Ollama for easier calls
- Context engineering
- Agent memory harness
- Multi-agent orchestration
- LLM as a judge
These skills are more focused towards inference engineering to ensure that your model runs smoothly in production.
Memory, inference, and orchestration matter a lot more than frontier models.
I am learning inference engineering and agent harness nowdays.
Totally worth exploring!
In the era of vibe coding, study the fundamentals and read books to keep your fundamentals clear. My career has benefited a lot from reading these books:
1. Designing Data Intensive Applications
2. Operating Systems: Three Easy Pieces
3. A Philosophy of Software Design
4. System Design Interview: An Insider‘s Guide
I came across this blog from Chip Huyen in 2022 where she discussed the ai courses to do. i feel they are relevant to this day!
Here they are -
STATS 202 :- data mining basics
CS109 :- probability foundations
CS231N :- computer vision DL
CS224N :- NLP deep learning
CS229 :- theoretical machine learning
CS221 :- intro artificial intelligence
CS228 :- probabilistic graphical models
CS234 :- reinforcement learning
CS238 :- decision theory RL
CS224W :- graph machine learning
CS246 :- large-scale data mining
CS230 :- applied deep learning
CS236 :- generative models
EE263 :- linear dynamical systems
CS336 :- robot perception
CS103 :- discrete math foundations
CS124 :- NLP basics
CS223A :- robotics fundamentals
i have done a few from here, would reccomend
“Build an AI agent” isn’t the right starting point.
Real systems need architecture, determinism, and integration—not just better prompts.
Here’s what actually matters: https://t.co/EQ3bs5eYgI
AI Engineering Buildcamp is project-driven.
You learn AI engineering by building.
15 projects you can build during the course 👇🏼
(You get lifetime access to the course if you sign up)
Don’t overthink it.
• Build a Calculator to master logic & loops
• Build a Weather App using live APIs
• Build a CRUD Web App with Flask + DB
• Build a Chatbot UI with Streamlit + GPT
• Build a File Organizer with os & shutil
• Build a Resume Parser using NLP
• Build a Stock Predictor using ML
• Build a Job Tracker that updates Notion
If you're hunting for remote work, Reddit is one of the most underrated job boards on the internet.
You just need to know where to look.
Bookmark this thread ↓
If I had to start System Design from scratch again, I’d ignore 90% of the internet…
…and just study these 40 articles.
No random YouTube hopping.
No endless tabs.
No confusion.
Just a clean, structured path that actually works.
This is the roadmap I *wish* I had during my interview prep 👇
You’ll learn:
• How to think in systems (not just memorize answers)
• Real trade-offs (scalability vs consistency, latency vs cost)
• How to design like a senior engineer
And the best part?
You can even:
→ Ask questions via voice in real-time
→ Get instant feedback
→ Practice HLD even as a beginner
Here’s the full breakdown:
1. HLD Basics → https://t.co/I6E3xWnnPV
2. Core Concepts & Trade-offs → https://t.co/guimX30aqb
3. Networking & DNS → https://t.co/mEQN53UdRK
4. Load Balancing & Scaling → https://t.co/kKWa0cgDfU
5. Application Architecture → https://t.co/pBzsfCiUVC
6. Databases → https://t.co/Aq4AJBSTWy
7. Caching → https://t.co/SjJ4m8qhhP
8. Async Processing → https://t.co/1S25lPgiEC
9. Communication Protocols → https://t.co/v5Lse3k0wP
10. Performance & Monitoring → https://t.co/eQOXMGYVqj
11. Cloud Design Patterns → https://t.co/20nRBjreAn
12. Reliability Patterns → https://t.co/bWuWBbzqEZ
Save this.
This is easily 50+ hours of scattered learning—compressed into one roadmap.
Follow this, and System Design will finally start making sense.
If you’re hunting for a remote job, you just need to figure out how Reddit works, and you’ll never be unemployed for a long time.
Here’s a list of subreddits you should bookmark right now:
In 2013, Yale professor Ben Polak gave a legendary 1-hour lecture on Game Theory.
It will change how you make decisions in negotiations, business, and life.
His frameworks:
• Dominance arguments
• Backward induction
• The proactive bias
12 lessons to make better decisions:
You don't need random papers, just learn these concepts sequentially to kickstart your LLM engineer journey.
Here’s what actually matters if you’re an engineer:
- Tokenization and embeddings
- Attention and transformer blocks
- Training and fine tuning
- LoRA and QLoRA
- DPO and alignment
- Quantization
- KV cache and inference systems
- FlashAttention and PagedAttention
Not theory.
Systems.
I wrote a complete guide covering everything step by step. If you want to build with LLMs, not just study them, this is for you.
Also breaking down inference and deployment at scale in upcoming posts on hands-on level.