How to Become an AI Engineer in 2025: Skills, Roadmap & Career Guide

What Does an AI Engineer Do?

An AI Engineer is responsible for tasks that use artificial intelligence concepts and create projects at hand. Basically, the role revolves around working with, designing, building, and optimizing machine learning (ML) and deep learning models that solve complex problems at scale.

As an AI Engineer, you will also be required to combine data, algorithms, and infrastructure to deliver usable AI-powered solutions. You might have to often collaborate with data scientists, software developers, and product teams to bring these solutions into production.

Core responsibilities of an AI engineer include:

• Building data pipelines
• Training and fine-tuning models
• Deploying models into production
• Optimizing performance
• Developing AI applications

Along with these core responsibilities, here are some key responsibilities that you must keep in mind:

Key Responsibilities of an AI Engineer

AI engineers are required to handle both the technical and practical aspects of deploying artificial intelligence. Their main responsibilities include:

• Building and training ML models: Designing algorithms, fine-tuning models, and ensuring accuracy.
• Working with big data and cloud infrastructure: Managing large datasets and using platforms like AWS, GCP, or Azure for better AI solutions.
• Collaborating with product and data teams: Working closely with data scientists, analysts, and product managers to align AI models with business needs.
• Optimizing AI for real-world applications: Improving model efficiency, reducing costs, and ensuring reliable performance in production.

These responsibilities vary from company to company in accordance with their needs.

Everyone who finds this job interesting usually has one question in mind: “How to begin?”

Worry not, as we have jotted down 6 easy steps that you can use to begin your journey as an AI Engineer!

Step-by-Step Roadmap to Becoming an AI Engineer

AI engineering may sound overwhelming at first, but if you follow a balanced and doable plan, the path becomes much more achievable. Here’s a realistic roadmap to help you move from beginner to professional:

Step 1: Get the Right Educational Foundation

Most AI engineers start with a Bachelor’s degree in Computer Science, IT, Data Science, or related fields. This gives you exposure to programming, algorithms, and mathematics.
If you’re from a non-technical background, don’t worry, online courses and bootcamps can help you provide the foundation you need.

Step 2: Master Core Technical Skills

Strong technical skills are essential for an AI engineer.

• Programming: Python is the primary language, but R, Java, or C++ are also useful.
• Mathematics: Focus on linear algebra, probability, and statistics. These are essential for understanding ML models.
• Frameworks: Learn to use tools like TensorFlow and PyTorch, which power most AI applications.

Step 3: Gain Relevant Experience

A strong portfolio showcasing your skills adds immense credibility.

• Start with smaller projects like chatbots or recommendation systems.
• Explore domains such as NLP, computer vision, or fraud detection.
• Participate in Kaggle competitions, contribute to GitHub, or apply for internships to build job-ready skills.

Step 4: Earn Certifications & Advanced Training

Certifications validate your skills and can even make you a desirable candidate. Programs like DeepLearning.AI’s Machine Learning Specialization, Coursera’s AI Engineer Professional Certificate, or Scaler’s AI & ML program can accelerate your learning and add credibility to your resume.

Step 5: Build a Portfolio & Network

Employers value proof of work.

• Showcase your projects on GitHub and LinkedIn.
• Contribute to open-source AI projects to gain visibility.
• Attend hackathons, webinars, and AI conferences to expand your network and learn from peers.

Step 6: Apply & Grow

Once you’ve built skills and a portfolio, you can start applying for roles such as AI Engineer, ML Engineer, or NLP Engineer. With experience, you can grow into mid-level positions and later transition into senior roles like AI Architect or AI Product Manager.

Consistency matters more than speed. Break your learning into manageable goals, and keep updating your skills as the field evolves.

Explore Scaler’s AI & ML Engineer Courses to follow a structured learning path and get guidance from industry experts.

Skills Required to Become an AI Engineer

To succeed as an AI engineer, you need a mix of technical expertise, strong analytical foundations, and soft skills that help you work effectively with your tasks as well as your team.

Here we have listed some of the skills you should definitely work on:

Technical Skills

AI engineers rely heavily on programming and tools, and the ones listed below are currently valuable in workplaces.

• Programming Languages: Python is the most widely used, but R, SQL, and C++ can also be used depending on your task.
• ML Frameworks: Practical knowledge of TensorFlow, PyTorch, and Keras is essential for building and deploying models.
• Big Data Tools: Frameworks like Hadoop and Spark help handle massive datasets efficiently.
• Cloud Platforms: Familiarity with AWS, Google Cloud, or Azure is important, as most AI models are trained and deployed on cloud infrastructure.

Mathematical & Analytical Skills

Trust us when we say, mathematical knowledge and analytical skills are the crux of learning AI, especially on a fundamental level. The topics listed below are suggested to cover for a deeper understanding of the role and subject.

• Core areas: Having a good grasp of probability, linear algebra, and statistics can make machine learning easier to understand.
• Algorithms & Optimization: Knowing how algorithms are designed and optimized allows you to build efficient AI systems.

Soft Skills

We understand that practicing technical skills is a huge task in itself, but having soft skills is equally important and does require your time and attention. Here are some skills you should surely work on:

• Problem-Solving: Try to frame real challenges into AI problems.
• Critical Thinking: Evaluating models, spotting biases, and improving accuracy.
• Communication: Explaining technical insights to product managers, business teams, or clients in simple terms.

We can see how an AI engineer doesn't only require coding, but they’re also expected to have a mix of skill sets that make working on tasks and collaborating with teams better.

AI Engineer Salary Insights

So is the pay worth the grind? Let’s check out:

India: AI Engineer Salary

According to Glassdoor:

• On average, AI engineers in India make about ₹12.2 LPA.
• Freshers or those at the lower end of the spectrum usually earn around ₹6-7 LPA.
• With a few years of experience and specialization, salaries rise to ₹18-20 LPA.
• The most experienced or niche experts can go beyond ₹30 LPA in India.

USA: AI Engineer Salaries

In the U.S., Glassdoor data shows salaries ranging from $147K for AI Engineers to nearly $280K for senior roles. Top-paying cities include San Francisco, New York, and Seattle, where demand for AI talent is especially strong.

Just like in the U.S, many more countries provide remote roles for AI engineers; it is always best to keep a lookout for such companies.

Career Path & Growth Opportunities

The journey of an AI engineer typically begins with roles such as AI Engineer or Machine Learning Engineer, where the focus is on building and deploying models. With 3-5 years of experience, professionals can move into Senior AI Engineer positions, leading projects and mentoring junior team members.

From there, the career ladder often progresses to AI Architect, where the responsibility shifts to designing large-scale AI systems, integrating cloud platforms, and aligning solutions with business goals. At the highest level, professionals can aim for Head of AI or Chief AI Officer roles, driving organizational AI strategy and innovation.

Growth opportunities aren’t limited to technical leadership. Many AI engineers branch out into AI research, focusing on cutting-edge advancements in areas like generative AI, robotics, and reinforcement learning. Others transition toward AI product management, where technical expertise and business acumen are used to build AI-driven products for industries such as healthcare, fintech, and edtech.

With demand growing across sectors, AI engineers have one of the most versatile career paths in tech, offering both technical and strategic leadership opportunities.

Best AI Courses & Programs to Kickstart Your Career

It is confusing for many people to understand where to begin, and of course, it’s a crucial part of building your career. At Scaler, we understand the various dilemmas students and even working professionals sometimes face.

So, here are some practical learning paths to consider:

• Degree Programs (B.Tech/M.Tech in AI, Data Science, CS): If you’re still in college or planning higher studies, formal degree programs will give you a strong foundation in programming, math, and AI fundamentals. They take longer but open doors to research and specialized roles.
• Bootcamps & Certifications: You can check out AI/ML programs in platforms like Coursera, DeepLearning.AI, and Simplilearn are great if you want a shorter, skill-focused option. Bootcamps help you pick up technical expertise quickly and are especially useful for professionals looking to switch into AI.
• Scaler AI & ML Program: If you’re aiming for structured learning with career support, Scaler’s program is a strong choice. It combines technical training with mentorship, industry projects, and placement guidance. Our students have made great use of our resources, and if you are someone who wants the same, then do check out the course outlets on our website.
• Specializations (NLP, CV, Generative AI): Once you’ve built the basics, you can focus on areas like natural language processing for chatbots, computer vision for image/video recognition, or generative AI for prompt engineering and LLMs.

No matter which route you take, make sure to pair learning with credible projects and a strong portfolio.