Top 7 AI Engineer Roles for 2027: Skills & Salaries | Full Breakdown & Transcript

[00:07] learn. In today's video, we are going to explore top AI engineer roles for 2027. We all know that AI is now part of the apps and tools we use every day and behind all of it are engineers who actually build it, run it, and improve

[00:21] it. But AI engineer is just not one job. It is a group of roles and each one has something different. In this video, we will look at the seven roles and each one of them we are going to learn about what the job is and what are the skills

[00:36] needed and what is the salary you can expect. Now, before we move on, here is the agenda of our today's session. First, we are going to discuss about why AI engineering is in demand. Then, we will explore the top seven AI engineer

[00:48] roles. After that, we are going to explore the AI engineer salary and then finally, we will discuss about which role fits you the best. Now, before we move ahead, just a quick info, guys. If you'd like to go beyond the concepts and

[01:01] learn to build AI applications, then you can explore simply learn's advanced executive program in applied generative AI in collaboration with IITM Pravartak and Microsoft. This is a 5-month program and it covers rag agent workflows, MCP,

[01:14] multi-agent systems, and MLops. And you are also going to get hands-on practice using 29 plus tools and framework. You'll also work on real-world project. course. The course link is mentioned in the description box. Now, here is a

[01:28] short quiz to test your knowledge. Which search algorithm guarantees to find the shortest path to a goal state in an unweighted graph? And your options are depth-first search, breadth-first search, greedy best-first search, or

[01:42] depth-limited search. Please mention your answers in the comment section below. So, let's get started. So first, let us try to understand why AI engineering is in demand. Companies are adding AI to almost every product and to

[01:56] it. The numbers show this clearly. LinkedIn ranked AI engineers as the number one fastest growing job title in the United States for 2026. Job posts for AI engineers went up to

[02:10] 143% in 2026. And eight out of 10 businesses leaders say that they would rather hire someone who's good with AI tools, even >> [snorts] >> So, with this data, we come to know that

[02:26] the demand is really going for the AI engineer roles. Now, we are going to see the top 10 AI engineer roles. And these roles are AI engineer, ML engineer, GenAI or LLM engineer, AI agent or AI application engineer, ML ops engineer,

[02:41] computer vision engineer, and AI research scientist. We will explore these roles one by one. So, first, let us discuss about the AI engineer roles. In simple word, AI engineer takes AI models that already exist and turn them

[02:56] into useful features inside apps. They don't usually build a model from scratch. Instead, they find the right model and make it work for the The work usually follows four step.

[03:08] First, pick up an AI model that fits the task. Next, connect it to the company's data. Then, build it into an app that people can use. And finally, test it and keep improving it. Now, you'll be wondering what it takes

[03:21] to become an AI engineer. Let's go through the skills one by one. So, the key skills are Python, first of all, which is a primarily programming language for this. Next, you should have an idea about

[03:35] APIs. Next, LLMs. After learning about the LLMs, I would suggest to you to explore the cloud platforms like AWS, Azure, or GCP. Uh you can start with Azure. That's a good one. And uh you can also choose AWS,

[03:50] Then, learn a version control system such as Git. And finally, try to see the prompt design templates which are present and you can use OpenAI's SDK for the same or you can use Anthropic's

[04:06] prompt design templates. Next one we have is machine learning engineer role. Now, what a ML engineer actually does? An ML engineer teaches computer to learn patterns from the data. For example, a bank can use a model to spot a fraud

[04:21] payment at the moment it happens. And the work runs in a cycle. First, you collect the data, then clean it, then you train the model, and next you test the results to see how well it works. And finally, you deploy it and keep

[04:34] watching it. If your model is showing any drifts, so it can also predict that. Now, basically, what are the key skills required for this role? First one we have Python, then statistics, then you have to learn about

[04:48] I would suggest you learning scikit-learn and then you can go for PyTorch. Apart from that, what you can do is you can also look for Databricks, tools, and also learn one cloud model, which is

[05:05] very much important and see how well you are integrating it. Next role that we have all the way here here is generative AI engineer role. This role is also called as LLM engineer role. So, a generative AI engineer basically builds

[05:19] tools like chatbots, writing helpers, and smart search using large language models or LLMs. Let's take a simple example, a help desk chatbot for the company. So, the first user asks the question, next the system searches the

[05:33] company's document for the right information, then the LLM writes the answer, and finally the user gets the clear answer along with the sources. RAG, which stands for retrieval augmented generation. It lets the AI

[05:48] knowledge source. Now, you'll be wondering what it takes to become a GenAI engineer. So, first I would say Python is a base as it is for most of the AI roles. Then I would suggest you learning LLM APIs, which

[06:04] app to models like GPT, Claude, or Gemini. RAG, definitely you should master this. Very, very much important. Then learn about vector databases, where

[06:16] you store the documents. And finally, explore LangChain, LangGraph, or you can explore AutoGen, which is also very amazing framework. Now, let us explore the next role, which is an AI agent engineer.

[06:31] This is one of the newest roles in the field. We all know that AI agent does not just answers. It plans steps and uses tools to get the task done for you. chatbot actually answers the question you ask. For example, let's say if you

[06:46] ask, "What is your leave policy?" it is going to tell you. But an AI agent gets the task done. Let's say you say you apply leave for Friday and tell my manager. It is going to do both of the task. So, you'll be wondering how does

[07:00] an agent works. So, basically it starts with a goal, then it makes a plan, next it uses tools like calendar or email app, and then it checks the result. And it repeats these step until the job is done. Now, what it takes to become an AI

[07:14] agent engineer? So, an AI agent engineer designs, builds, and tests these agents. And what does it takes? So, again Python is the starting point, then explore LLM

[07:26] APIs, which you are going to give to the agent. Tool calling is very much important. Then you teach the model when to use which tool. After that, work on the workflow design, which means breaking a big task into clear steps so

[07:41] that the agent can follow. And testing is also very much important here because an agent is going to take real actions. So, you need to make sure it does the right thing every time. This role usually comes after GenAI experience.

[07:55] So, this is a good step once you know how LLMs works. Now, let us explore the next role that is ML Ops engineer. An ML Ops engineer keeps AI models running smoothly after the launch. Think of a racing car. The driver gets the

[08:10] attention, but the pit crew keeps the car running. So, ML Ops engineers are car running. So, ML Ops engineers are the pit crew for AI models. So, without ML Ops, a model may run on just one laptop. But let's say when something

[08:24] laptop. But let's say when something goes wrong, no one can spot the problem. And every update is slow and risky. With ML Ops, the model runs live for real users, alerts go off when there is a problem, and any updates go out fast and

[08:38] safely. So, what it takes to become an ML Ops engineer? First, I would say learn about Docker, which is very much important. Then explore CI/CD because you'll be doing continuous integration and development. Then, I

[08:51] would suggest to you learning Databricks, which is very very much popular. Also, explore bit of DevOps side because this is most of Ops side role. And also learn about machine learning algorithms and

[09:06] Git as a version control system. Now, at number six, we have computer vision engineer. A computer vision engineer teaches the computers to understand photos and videos. You already use their work every day. For example, the face

[09:20] unlock feature on your phone. It knows that it's you. In factories, cameras spot damaged products on the line. Or in hospitals, these tools helps doctors read X-rays. So, you'll be wondering what it takes to become a computer

[09:34] vision engineer. So, Python is your main language, then I would say explore OpenCV, which is a Python library especially important for working with images. PyTorch is also very much important, which is used to build and

[09:48] deep learning models. And then you can explore CNN or convolutional neural which is very much important algorithm for understanding the images. Many computer vision engineers start with general machine learning and then

[10:02] degree in computer science or electronics and a good first project you can start to show your portfolio. It could be a model that sorts photos into groups like cats and dogs or it detects faces in the video. So, you can

[10:19] detects faces in the video. So, you can build your portfolio like that and share your links in your resumes or you can post it on the LinkedIn where you have done these projects. So, I hope so you would have got a brief

[10:31] idea regarding computer vision engineer. Now, let us explore the next role, which is one of the most hottest role in AI. A research scientist invents new AI methods. They work with what later becomes the tools that engineers use it

[10:46] A research scientist's main goal is to invent new AI methods. While AI engineer is going to use those tools to build the products. A researcher spends the day running experiments while an engineer writes and

[11:00] ships the code. Researchers often have the master degree And for engineers, a degree or a strong project is usually enough. scientist? So, first of all, advanced math is the

[11:14] basic foundation. I would suggest you starting with linear algebra, calculus, probability because new AI methods are built on top of them. Then deep learning is the main area of most of the AI research today.

[11:27] PyTorch is a tool most researcher use it to test their ideas. And also, you need to read research papers regularly to keep up with the new work in the field. This role usually needs more formal study than the others.

[11:41] Many researchers have a master degree or a PhD. And that is also a good way to start your career in this domain. And if you love maths and new ideas, then research could be the path for you. So guys, here is this AI engineer

[11:54] salary, primarily focusing India as a region. So freshers, you can expect somewhere about 6 to 8 LPA. Mid-level means 3 to 6 years of experience, it could go from 12 to 25 LPA. Senior-level, you have 25 to 45 LPA. And

[12:09] at the top roles, you can earn up to 80 LPA. So guys, the salary depends upon the organization, the location, and how much years of experience you are having. So once at a glance, if you like building apps, you can go

[12:24] for AI engineer role or gen AI. If you want to work with data and the numbers, same. If you want to automate task, AI agent to work with servers in the cloud, which has a little bit of DevOps in that,

[12:39] MLOps is the perfect choice. Let's say if you want to work with the images, like photos and the videos, so computer vision engineer. And finally, if you are focusing on research, then AI research scientist is

[12:51] Now, let us know in the comment section below which role are you targeting for in 2027. Thank you guys for watching this video. So guys, these were some of the top AI roles which you can look out for in

[13:05] Till then, keep learning and keep growing.