Let’s cut to the chase: AI isn’t just the future—it’s right NOW.

And if you’re not building skills that’ll keep you relevant in 2025 and beyond, you’re playing career roulette.

The good news? You don’t need a PhD or a Silicon Valley pedigree to win this game.

I’ve spent the whole 2024 digging into industry reports, talking to engineers at top AI firms, giving talks working with clients, and reverse-engineering what are the key skills that AI engineers need.

What you’re about to read isn’t theoretical fluff—it’s the exact playbook that I shared with my top tier clients, and it’s also companies like OpenAI and Anthropic are using to train their teams right now.

These 14 skills are your golden ticket. Master them, and you’ll be the person companies fight to hire when they’re building the next ChatGPT or self-driving car.

Let’s dive in.

1. Prompt Engineering (Yes, It’s Still a Thing)

“Just talk to it like a person!” they said.

“It’ll be easy!” they said.

Then reality hits: 73% of AI projects fail because teams can’t consistently get useful outputs from LLMs.

The secret sauce? Structure your prompts like you’re briefing a brilliant but literal-minded intern.

Start with:

The Ask: “Write a Python function to calculate Fibonacci sequences”

The Rules: “Use recursion, include docstrings, no external libraries”

The Example: “Here’s how we did it for factorial calculations: [code snippet]”

Tools like LangChain’s prompt templates are game-changers here.

And no, ChatGPT won’t make this skill obsolete—the best prompt engineers are getting $375K/year because they know how to make AI sing.

2. RAG – Your Superpower

Retrieval-Augmented Generation (RAG) isn’t just a buzzword—it’s how you make your AI smarter, and stop it from hallucinating 

Think of RAG as giving your AI a research assistant. You’re combining:

• Vector Databases (ChromaDB, Pinecone)
• Embedding Models (BERT, GPT-4)
• High quality custom data

A good use case will be a financial analysis bot  pulls latest financial report from securities commissions before commenting on the company’s performance.

Companies that use RAG also see 40% fewer errors in factual responses. 

Pro tip: Start with LlamaIndex to prototype RAG systems faster.

3. Fine-tuning Models

Sometimes, the models don’t quite behave the way we want it to be. It already has the knowledge, just that the way it presents is not really what we want.

What you need to do is to retrain the model, but you soon realise it is 

1. Hard to collect, clean and manually label the data

2. Just use the old model

So, in order to take an existing model, nad make it sounds like us, we need to fine tuning.

Pro tip: Fine-tune DeepSeek-7B on your company’s support tickets to create a customer service officer that knows your product better than anyone in the company.

4. AI Agent Orchestration

Single AI models are so 2023. The real magic happens when you create AI teams:

Creating AI agent workflow is not hard. We can use tools like LangGraph for building workflows, CrewAI to create multiple roles, and n8n for automation.

What is more essential is the mindset. Think of the whole company run in workflow, break down into processes, and clearly define the input and output, so that every building block is an AI agent, and they can run without any human intervention, able to fall back when an error occur.

Use case: An e-commerce company automated 89% of customer service by creating a squad of specialized agents for returns, upselling, and tech support16.

5. Multimodal Mastery

Text-only AI is just the beginning. The future is models that juggle:

Vision (DALL-E 3, Qwen-VL)

Audio (Whisper, Bark)

Video (Sora, Dreamina)

Real-world win: A real estate app that converts phone call audio → meeting notes → 3D virtual tours. Users convert 2.3x faster5.

6. Edge AI Deployment

Cloud is for easy and convenient. But there are edge cases where running cloud is not ideal, or impossible.  For instance:

Devices that has limited computing power  (Smart watches, IoT devices)

A farm with no internet or 4G connections

A bank where no external connection is allowed to connect ot the database.

A hospital where patient database is not allowed to upload to external databases.

That’s why you need to know how to deploy AI to IoT Devices (NVIDIA Jetson), servers, small scale PC like Mac Mini.

Case study: A manufacturing plant saved $2M/year by running defect detection AI directly on assembly line cameras instead of sending data to the cloud

7. No-code AI Tools

Don’t like to write code and debug? No problem.

The rise of no-code AI tools has made AI engineering more accessible than ever. You can automate tasks, analyze data, and deploy AI models—all using drag-and-drop UI.

Here are some tools for you to master:

• n8n
• Knime
• Make.com

A Google Sheets automation using n8n.

These tools let you connect AI and machine learning services like LEGO blocks—building visual workflows with little effort.

One of my students used KNIME to create a no-code system that helps secondary school teachers to mark assignments, highlight mistakes, and provide feedback—all without writing a single line of code.

AI engineering isn’t just for programmers anymore. It’s for builders.

8. Python

If no-code AI tools are like LEGO blocks, Python is the full workshop. It gives you flexibility to build what you need.

Python takes AI engineering to the next level. It lets you fine-tune models, optimize performance, and build custom AI solutions that we can’t do using a drag-and-drop tool.

One of the biggest advantages of Python over no-code tools is its ease of versioning and collaboration.

We can store our code in a GitHub repository, making it easy to manage access, track changes, and maintain security within a team.  This ensures seamless collaboration, allowing multiple engineers to work on the same project without conflicts.

Python is also highly versatile.

It simplifies the process of building APIs, making it easier to integrate AI into real-world applications.

It also supports the development of simple front-end applications using tools like Streamlit and Gradio, allow us to create interactive AI apps with minimal effort.

Trade secrets: The best AI engineers know both. They use no-code for rapid prototyping and automation, then switch to Python when they need more control.

9. App Development

If you don’t mind coding, or you already can code, Python is a good option.

The main reason choosing a programming language over a no-code tool is the ease of maintaining version.

It is also versatile for creating API (Application Programming Interface), and also simple front-end applications using Streamlit or Gradio.

10. AI Ops

AIOps is the bridge between AI and real-world deployment. Without it, AI systems fail silently. With it, AI runs like a self-optimizing machine.

In 2025, AI Engineers who understand AIOps won’t just build models—they’ll make sure those models actually deliver results.

Where to learn these skills?

To start building up these skills, you may start with:

1. Building projects

From Huggingface or Github

2. Certification

AI Engineering Certification

Also, you might want to consider joining our mailing list where we regularly run free workshops and webinars to keep you updated on the latest industry news.

3. Take tutorial classes

Deeplearning.AI, 

You really don’t have to follow tightly on what are the new tools, new models, but rather, 

Conclusion

This list is neither exhaustive or confirmation. 

It is to give you some indicators about the essential skills that you need when working in AI for 2025. 

Also, you don’t need to master all of them. You need to combine creatively them depends on your industry.

Oil and gas: Edge computing

Things are changing quickly.