A number of software, finance, healthcare and retail organisations across the globe are now using AI technology. A 2025 global survey by McKinsey found that 88% of respondents said their organisations used AI in at least one business function.
Note that learning to use an AI tool is not the same as becoming an AI engineer. To become an AI engineer, one needs to develop skills in programming, mathematics, data and software engineering in the right sequence. Students must also learn how to convert an AI model into a reliable application.
What Does an AI Engineer Do?
An AI engineer develops and maintains software systems based on Artificial Intelligence. The work may include:
Understanding the problem and gathering data
Training a model or choosing an existing model
Connecting models to applications using APIs
Checking the accuracy, safety and quality of responses
Deploying and improving AI systems
For example, to build a customer-support assistant, an AI engineer may prepare documents, choose a model, build a retrieval system and debug cases where the AI generates incorrect responses.
AI Engineer Roadmap at a Glance
Stage | What to Learn | Practical Outcome |
1 | Python and Computer Science | Build basic applications |
2 | Mathematics, SQL and data | Prepare and analyse datasets |
3 | Machine learning | Train and evaluate models |
4 | Deep learning and Generative AI | Build neural network and LLM applications |
5 | APIs, cloud and MLOps | Deploy and monitor an AI system |
6 | Projects and experience | Build a portfolio and prepare for jobs |
This AI engineer roadmap is not time-bound or specified in months. A student should proceed to the next step only after gaining enough practical experience at the previous stage.
Step-by-Step Roadmap on how to become AI engineer
Step 1: Learn Python and Computer Science Fundamentals
Python is one of the key languages used in many areas of AI. It supports data analysis, machine learning, automation and building applications.
First, beginners should learn to work with variables, control structures such as if/else, loops, functions, object-oriented programming, file handling and debugging. In addition, they should become familiar with common data structures such as lists, dictionaries and sets, basic algorithms such as searching and sorting, Git, GitHub, databases and APIs.
AI engineers do not work only in Jupyter Notebooks. In addition to writing application code, students must also understand how to structure code and connect different software components and tools.
Practical milestone: Create a simple Python application and upload it to GitHub.
Step 2: Build Mathematics and Data Foundations
Students can start coding without knowing advanced mathematics and learn it step by step.
Linear algebra: Vectors, matrices and transformations
Probability and statistics: Distributions, correlation, sampling and variance
Calculus: Functions, gradients and basic optimisation
Students should develop enough programming knowledge to apply it to data analysis. They should also learn to use common tools such as NumPy and Pandas for numerical calculations and data manipulation, and SQL for storing and retrieving data.
Practical milestone: For a publicly available dataset, develop and carry out a method of analysis and document the data-cleaning decisions, identified patterns and limitations in a written report.
Step 3: Learn Machine Learning
Machine learning helps computers learn from data and make predictions by identifying patterns.
For a student starting to learn machine learning, it is useful to begin with supervised and unsupervised learning and then move to regression, classification and clustering. The next step would be to learn algorithms such as decision trees and random forests.
Beginners should also understand concepts such as accuracy, precision, recall, F1 score, overfitting and cross-validation. Scikit-learn is a good starting point for applying machine learning with Python.
Practical milestone: Train more than one model for the same problem and explain why one was selected over the others.
Step 4: Learn Deep Learning and Generative AI
Students can then move on to deep learning. They learn how neural networks are applied to images, speech and text. They should understand different layers, loss functions and optimisers using either PyTorch or TensorFlow.
After learning the basics of deep learning, students can move into the world of Generative AI.
Large language models
Prompt design
Embeddings and vector databases
Retrieval-augmented generation
Model APIs and tool calling
AI agents
Evaluation for accuracy, hallucination, safety and cost
Practical milestone: Build a document-based question-answering system that retrieves answers from reliable sources.
Step 5: Learn Deployment and MLOps
A model becomes valuable when people or applications can access and use it reliably.
Students should learn:
APIs and model serving
Docker and basic cloud deployment
Testing, logging and versioning
Experiment tracking and monitoring
Model drift and data drift
It is also important for students to gain a basic understanding of privacy, bias, explainability, copyright and various security risks associated with AI. As a beginner, it is better to start with one cloud platform instead of trying to learn AWS, Azure and Google Cloud at the same time.
Practical milestone: Publish an AI application so that others can access and test it.
Step 6: Build Projects and Gain Experience
More value is created by completing a handful of real projects than by having many copied projects. A beginner’s portfolio can contain the following projects:
A machine learning project such as churn or fraud prediction
A deep learning project such as image or text classification
A Generative AI project such as a document-search assistant
Each project should explain the problem, data used, model choice, evaluation, limitations and deployment method. Projects should start with the problem that the technology is trying to solve.
A report by The Tribune described how two 20-year-old students from Scaler School of Technology, Advith Sharma and Arsh Gupta, created an AI-powered insurance platform. They investigated the problems faced by insurance agents in relation to policy renewals, policy documentation and customer servicing.
Students can gain experience through internships, hackathons, open-source projects and tools built for real users. Internships in Python, backend development, data or software can also be useful.
Skills Required to Become an AI Engineer
The main technical skills of an AI engineer are Python, SQL, data structures, machine learning, deep learning, Generative AI, APIs, Git, cloud deployment and MLOps.
Other key skills include problem-solving, debugging, testing, communication, documentation and product understanding.
What Should You Study to Become an AI Engineer?
When looking at how to become an AI engineer, there is no single academic path. Most programmes initially focus on Computer Science, mathematics, data structures and software development.
When comparing different programmes, students need to check whether the curriculum includes:
Basic programming concepts
Data structures and algorithms
Databases and SQL
Probability, statistics and linear algebra
Machine learning and deep learning
Generative AI
Software development
Cloud computing and deployment
Hands-on projects
Internships and industry exposure
Students often ask is AI a branch of engineering. AI is typically taught within Computer Science as a specialisation or as part of a broader technology programme.
Students can explore the CS & AI Programme at Scaler School of Technology. The 4-year programme follows a learn-by-building approach and trains students in Computer Science foundations and AI by building 50+ projects and gaining industry exposure.
36% of SST’s first batch received confirmed (intern-to-pre-placement) offers before entering the 4th year, 55% of those placed students got into AI-native companies.
Students can watch this discussion on the impact of AI on Computer Science education.
How AI is Changing Computer Science Education?
AI Engineer Career Path
Students may start with an internship or junior role in AI, machine learning, Python, backend development or data. As they gain experience, students can move into AI engineering, Generative AI, MLOps, NLP or Computer Vision roles. Senior Applied AI roles may include AI Architect, Lead AI Engineer, ML Platform Engineer or Applied AI Lead.
Students can understand the broader AI engineering scope and its possible applications across different industries.
Conclusion
Learning how to become an AI engineer involves understanding problems, working with data, choosing the right methods and building reliable AI systems that produce useful results.
A good AI engineer roadmap is a structured learning path that starts with basic Python, Computer Science, mathematics and data knowledge. It then gradually moves through machine learning, deep learning, Generative AI and the deployment of AI for real-world problems through practical projects
FAQs
How can I become an AI engineer after Class 12?
To become an AI engineer, look for relevant undergraduate programmes such as Computer Science, AI, Data Science or Mathematics. Learn basic Python and mathematics first and then move into data handling, machine learning, Generative AI and deployment. Build complete projects during your studies and look for internships to gain real-world experience.
Which degree is best for becoming an AI engineer?
There is no single degree that is best for becoming an AI engineer. When choosing a study programme, students should pay attention to whether it helps them build a solid foundation in programming, Computer Science, mathematics, data, machine learning and software development through its curriculum, projects, internships and hands-on learning opportunities.
How long does it take to become an AI engineer?
There is no hard and fast rule for learning AI and developing the ability to build and evaluate complete end-to-end projects. It depends on the student’s background in programming and mathematics, the amount of time devoted to studying the subject and the quality of the projects completed.
