Fastest way to become an AI Engineer in 2026 | Skills, Projects & Salary

AI engineers are making more than $200,000 a year. And at companies like Meta and OpenAI, some are making more than 1 million. But here's the part that most people miss trying to break into AI

engineering. They're learning the wrong

engineering. They're learning the wrong skills in the wrong order, and wasting months on things that companies don't even hire for. But by the end of this video, you'll know what AI engineers actually do, what skills companies care

about, if you need math or ML for AI engineering, the projects that actually get you hired, and the fastest path [music] to being an AI engineer in 2026.

Hi friends, I'm Maddie. I'm a senior software who previously worked at Google and internet other big tech companies like Amazon, IBM, and Microsoft. For

this video, I reviewed a lot of job postings across LinkedIn, Indeed, and company career pages, read a bunch of articles and blogs, and chatted with a few friends who are currently working as AI engineers in foundational model

companies like OpenAI and Anthropic. I'm

not going to give you some theoretical path based on outdated advice. Instead,

I'm going to show you exactly what companies are actually hiring for and how to get there. Let's dive in. Before

we get into the road map, let's first talk about what exactly an AI engineer is. When people hear about AI engineer,

is. When people hear about AI engineer, they might picture someone with a PhD trading neural networks from scratch, writing research papers, or doing complex mathematics. That's not what

complex mathematics. That's not what companies are hiring for right now for AI engineering. To clarify, this road

AI engineering. To clarify, this road map doesn't make you an AI researcher or a deep learning scientist. It will

prepare you for AI engineer roles, the ones building LLM powered systems, not the ones training models from scratch.

There are over 500,000 open AI and ML engineering roles globally right now.

And most of them are looking for people who can build applications using existing AI [music] models, not people who create new models from scratch.

Think of it this way. A machine learning researcher is like someone who invents a new type of engine. An AI engineer is someone who takes that engine and builds an actual car people can drive. Both are

valuable, but they're completely different skill sets. And right now, companies are desperate for the people who can build the car for consumers.

When I analyzed those job postings, here are the technologies that kept on showing up. Python, prompt engineering,

showing up. Python, prompt engineering, rag, langchain, vector databases, and cloud platforms. That tells you exactly where the market is heading. First,

let's talk about the job market.

According to recent data, the median salary for AI engineers is around $242,000 per year. But that's just the median. For foundational model companies

median. For foundational model companies like OpenAI, senior AI engineers are making north of $700,000. The Bureau of Labor Statistics projects a 26% growth in computer and information research

scientist jobs through 2033, which is massively faster than most other occupations. AI related job postings

occupations. AI related job postings grew 25% in just the first quarter of 2025 compared to the previous year. And

the interesting thing is that nearly 40% of the most in- demand AI skills don't even exist in the current workforce yet.

That's a huge opportunity for anyone willing to learn. Now, let's get into the road map. I've broken this into four phases based on what I found in job requirements and what my AI engineer friends told me actually matters. Phase

one is all about building your fundamental skills. This is where most

fundamental skills. This is where most people either set themselves up for success or doom themselves to struggle later. First, if you're not already

later. First, if you're not already familiar with it, you need Python, not just tutorial style Python. You need to be comfortable writing production level code. Focus on data structures,

code. Focus on data structures, functions, working with Python and JSON and APIs, file handling, and error handling. Almost every AI tool, library,

handling. Almost every AI tool, library, and framework runs on Python. Second,

learn Git and GitHub. This also isn't optional. Every company uses version

optional. Every company uses version control and your GitHub profile could be a portfolio that hiring managers will check. Learn how to create repositories,

check. Learn how to create repositories, make meaningful commits, work with branches, and handle pull requests.

Third, get familiar with basic machine learning concepts. You don't need to be

learning concepts. You don't need to be an expert data scientist, but you should understand what a model is, the difference between training and inference, what embeddings are, and basic terminology. This vocabulary would

basic terminology. This vocabulary would be essential for communicating with your team and understanding documentation.

This phase typically takes 1.5 to 3 months. Definitely don't rush it.

months. Definitely don't rush it.

Mastering these fundamentals will save you months of frustration later. Next,

we move on to the LLM integration phase where you actually start working with AI. This is about learning to

AI. This is about learning to communicate and integrate large language models into applications. Start with

prompt engineering. This is probably the most underrated skill in AI right now.

Most people think it's just typing questions into chatbt, but real prompt engineering is about getting consistent, reliable results from models. You need

to understand system prompts, viewshot learning, chain of thought prompting, and output formatting. The difference

between a good prompt and a bad prompt can completely change the quality of your application. OpenAI and Enthropic

your application. OpenAI and Enthropic have good prompt engineering guides that you can check out. Next, learn to work with AI APIs. The Open AI API is the most common, but you can also explore Enthropic's cloud API and open source

options through hugging face. Learn how

to send requests, handle responses, manage tokens, and control costs.

Hugging face is especially important because it gives you access to thousands of open source models that you can run locally. This matters for companies that

locally. This matters for companies that can't send sensitive data to external APIs or want to reduce costs.

Understanding both closed source and open source options makes you way more valuable. This phase typically takes 2

valuable. This phase typically takes 2 to 3 months. By the end, you should be able to build a simple AI application that takes user input, sends it to a model, and returns useful results. Next,

phase three is building the actual AI systems where things get a bit more serious. This is what separates someone

serious. This is what separates someone who can play with AI from someone who can actually build production systems. First, you need to master Langchain.

This is the most popular framework for building LLM applications and appeared in a huge portion of the job postings that I analyzed. Lang Shank helps you connect models, tools, memory, and multi-step logic into cohesive

pipelines. Even a simple workflow

pipelines. Even a simple workflow teaches you how to structure the steps an AI takes to complete [music] complex tasks. Second, learn RAG, retrieval

tasks. Second, learn RAG, retrieval augmented generation. This is probably

augmented generation. This is probably the single most important pattern in enterprise AI right now. The problem it solves is that while LLMs know a lot about the general world, they know nothing about your own or your company's

specific data. Rag lets you give models

specific data. Rag lets you give models access to your documents, databases, and internal knowledge so they can answer questions accurately. You can take your

questions accurately. You can take your documents, split them into chunks, convert those chunks into embeddings, store them into a vector database like Pine Code or Chrome DB, and then when a user asks a question, you retrieve the most relevant chunks and send them to

the model along with the question.

Almost every internal AI assistant or enterprise chatbot uses this pattern.

Third, understand AI agents. Chat bots

give you text. Agents will let you perform actions like querying databases, calling APIs, updating records, sending emails, and triggering workflows. And

companies want AI that actually does work, not just an AI that will talk to you. Learning to set up these tools and

you. Learning to set up these tools and give agents the ability to take real actions is critical. Fourth, get

familiar with MCP or model context protocol. MCT is an open standard that

protocol. MCT is an open standard that lets AI models safely and consistently connect to tools, data, and services like Perplexity, GitHub, Google Doc, Zapier, Figma, and so many more. This is

so that agents can actually do real work, not just generate the text within the agent itself. This was first developed by Anthropic about a year ago, but has been getting increasingly important and was actually donated to

the Linux Foundation quite recently. It

creates a safe standard layer between your AI agents and external systems. Finally, learn basic LLM ops. Building

an AI system is one thing, but keeping it running reliably is another. You need

to understand prompt versioning, monitoring, cost management, and how to handle model updates. This phase

typically takes 2 to 3 months. Finally,

the last phase is about turning those skills into a job. You could have all the knowledge in the world, but without proof, no one's going to hire you. To do

this, you can start by building portfolio projects that showcase different skills. For example, you can

different skills. For example, you can build an AI decision support system using Rag. This would involve document

using Rag. This would involve document ingestion, chunking strategies, embedding, vector databases, semantic search, and context retrieval followed by structure generation with citations.

Instead of free form chat, the system will then output summaries, risk indicators, confidence scores, and source reasoning. This shows that you

source reasoning. This shows that you understand rag fundamentals, prompt templating, context window management, hallucination mitigation, eval strategies, and explanability. All core

enterprise AI concerns. Second, you

could build a natural language analytics system that connects LLMs to structured data through SQL. The system could take ambiguous user questions, perform intent classifications, generate SQL queries,

and execute them against a database, and [music] return charts, visualizations, tables, and narrative explanations. This

will demonstrate tax to SQL, schema reasoning, query safety, data validation, analytics workflows, and how to bridge LLM with traditional databases and BI style outputs. Third, you can build an AI workflow orchestrator with

agents, tool calling, and API integration. This system ingests inputs

integration. This system ingests inputs from multiple sources like tickets, emails, logs, or forms, performs classification, and prioritization, applies business rules, and executes actions across external systems. It also

includes things like logging, audit trails, and fallback logic. Projects

like this would show agent design, multi-step reasoning, automation pipelines, and real world system orchestration. For each project, you can

orchestration. For each project, you can write a clean readme, include an architecture diagram, and ideally record a short demo video. Make the code clean and well documented. You also consider certifications if you have time. For

example, the Azure AI engineer associate and data bricks generative AI engineer certifications are well recognized.

They're not required, but they can help you stand out in a competitive market.

And finally, in your resume, make sure to list your technical skills prominently. For example, Python,

prominently. For example, Python, LinkedIn, Rag, Vector Databases, the specific tools you used. You can link to your GitHub and make it easy for someone to see your work in 30 seconds if they want. To sum up this video, the AI

want. To sum up this video, the AI engineering field is moving fast. New

models, new frameworks, and new techniques are constantly coming out.

But this is actually good news for you.

It means that the people who start learning now and stay consistent will have a massive advantage. The

fundamentals I covered today, Python, prompt engineering, rag, agents, those are not going away. They're the

foundation that everything else builds on. So, pick one thing from this video

on. So, pick one thing from this video and start today. Don't wait until you feel ready. The best AI engineers I know

feel ready. The best AI engineers I know learned by building actively, making mistakes, and figuring out as they went.

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I'll be posting more videos about career tips and tech and AI insights. Thanks

for watching, and I'll see you in the next one.