AI/ML Roadmap for beginners in 2025

A step-by-step guide for software engineers to master essential skills and land a job in AI.

Feb 17, 2025

Every big tech company is going all-in on AI, and the demand for engineers with AI/ML expertise is growing faster than ever.

This guide is designed to help you break into AI/ML—whether you're a software engineer, data engineer, or simply curious about AI.

This is your step-by-step plan to build the right skills and land a job.

Why is AI/ML the Best Career Move?

I believe that transitioning into AI is one of the most impactful career moves you can make in 2025.

AI engineers are among the highest-paid professionals in tech. In March 2024, the median total compensation for AI engineers reached $300,600, a massive jump from $231,000 in August 2022 (Levels.fyi, 2024). AI-focused software engineers in the U.S. had a median total compensation of $270,000 in 2023, reflecting the increasing demand for AI talent.
Entry-level AI engineers earn 8.57% more than non-AI engineers.
Mid-level AI engineers earn 11.19% more.
Senior AI engineers earn 10.79% more.

These figures underscore how valuable AI skills have become and why transitioning into AI is a high-leverage career move.

As an engineer, working in AI will accelerate your growth, opens up promotion opportunities, and make you a highly sought-after candidate.

For example, when I left my software engineering job at TikTok, I was able to leverage my experience working on large-scale AI systems to secure multiple job offers from top tech companies.

You don’t need to be a research scientist or a machine learning engineer building cutting-edge models to break into AI. Nor do you need a PhD in deep learning, mathematics, or computer vision to build a successful career in the field.

The key to transitioning into AI is building a strong foundation in AI/ML concepts and learning how to apply them in real-world scenarios.

What is an AI/ML Engineer?

While the AI/ML engineer role is still evolving, it generally refers to a practitioner-focused role that blends software engineering with machine learning expertise. The responsibilities can vary across companies, but most AI/ML engineers focus on deploying and integrating AI into products.

Here is my definition given my current responsibilities and experience:

An AI/ML engineer is a software engineer who specializes in designing, building, and deploying machine learning models and AI-powered applications.
Unlike research scientists, who focus on developing new algorithms, AI/ML engineers apply existing models and techniques to solve real-world problems at scale.
Unlike machine learning engineers, who primarily focus on training and fine-tuning models, AI/ML engineers work on integrating models into production, optimizing AI systems.

Key Responsibilities of an AI/ML Engineer

Building AI-Powered Applications – Integrating machine learning models into production systems, such as recommendation engines, fraud detection, and NLP-based applications.
Optimizing AI Infrastructure – Scaling AI workloads efficiently with cloud platforms, GPUs, and distributed systems.
Deploying and Monitoring Models – Ensuring models perform well in production using MLOps best practices.
Data Processing and Feature Engineering – Cleaning, transforming, and preparing data to improve model performance.
Collaborating with Cross-Functional Teams – Working with data scientists, backend engineers, and product managers to deliver AI solutions.

You don’t need to specifically land a job titled AI/ML Engineer, as the term is broad and varies across companies. Many roles—such as software engineer, data engineer, machine learning engineer, or AI researcher—involve working in AI/ML, regardless of the job title.

AI engineering offers an exciting opportunity to work at the intersection of software engineering and artificial intelligence.

AI/ML Career Paths and Skillsets

Here are some of the key technical roles in AI:

1. Software Engineer (AI/ML Applications)

Focus: Building and integrating AI models into products, developing AI-powered features.
Skills: Python, C++, Java, AI/ML APIs, cloud services, backend development, model integration.
Example Jobs: AI/ML Software Engineer at Google, AI Product Engineer at OpenAI.

2. Machine Learning Engineer

Focus: Developing, training, and deploying ML models at scale.
Skills: TensorFlow, PyTorch, data pipelines, model deployment, MLOps, distributed computing.
Example Jobs: ML Engineer at Meta, Applied Scientist at Amazon.

3. Data Engineer (AI/ML Infrastructure)

Focus: Building scalable data pipelines to support AI/ML training and inference.
Skills: SQL, Spark, Airflow, cloud data warehouses, data lake architecture.
Example Jobs: Data Engineer at Netflix, ML Data Engineer at Microsoft.

4. AI/ML Infrastructure Engineer

Focus: Optimizing and scaling AI models for production, improving efficiency.
Skills: Kubernetes, Docker, distributed systems, GPU acceleration, cloud computing.
Example Jobs: AI Infrastructure Engineer at OpenAI, Deep Learning Engineer at Nvidia.

5. MLOps Engineer

Focus: Automating and streamlining the ML lifecycle, ensuring models are production-ready and maintainable.
Skills: CI/CD for ML, model monitoring, cloud orchestration, feature engineering, data versioning, Kubernetes, Docker, MLFlow, TFX.
Example Jobs: MLOps Engineer at Tesla, AI Platform Engineer at Google.

6. Research Scientist / AI Research Engineer

Focus: Advancing state-of-the-art AI techniques, developing new algorithms and models.
Skills: Deep learning, reinforcement learning, academic research, publications.
Example Jobs: AI Research Scientist at Microsoft, AI Scientist at DeepMind.

Step-by-Step AI/ML Roadmap

This roadmap is designed to help you break into AI/ML by providing a structured learning path, covering everything from foundational math and programming to advanced machine learning concepts and real-world applications.

Whether you're a software engineer looking to transition into AI or just getting started, this roadmap will give you the essential resources and practical steps to build the right skills and land a job in the field.

1. Learn Python and Core Libraries

Python is the dominant language for AI/ML. Almost every AI/ML framework, library, and tool is built in Python.

Key topics:

Intro to Python – Syntax, functions, loops, and OOP
Advanced Python – AI-specific Python concepts.
NumPy – Numerical computing and arrays.
Pandas – Data manipulation and analysis.
Matplotlib & Seaborn – Data visualization.
scikit-learn – Implementing ML algorithms.

Recommended Resources:

CS50’s Python Course – Beginner-friendly intro
Python for Data Science Handbook – Focuses on AI/ML applications

Difficulty/Timeline: (Beginner | 2-4 weeks)

Next steps: Once you’re comfortable with Python, move to Math for ML.

2. Build a Strong Math Foundation

A solid grasp of math is essential for understanding AI/ML algorithms. Focus on:

Linear Algebra – Matrices, eigenvalues, and vector spaces.
Probability & Statistics – Bayesian thinking, distributions, hypothesis testing.
Calculus – Derivatives, integrals, gradients, optimization.

Recommended Resources:

Essence of Linear Algebra (3Blue1Brown) – Best visual explanation
Khan Academy - Multivariable Calculus – Gradients & optimization
Introduction to Probability (MIT) – Covers probability essentials

Difficulty/Timeline: (Beginner | 4-6 weeks)

Next Steps: Once you’re confident in math, move to machine learning fundamentals

3. Learn Machine Learning Fundamentals

Get familiar with core ML concepts, models, and evaluation techniques:

Key topics to cover:

Recommended Resources:

Google ML Crash Course – Quick introduction to ML.
Machine Learning by Andrew Ng – The go-to foundational course.
The Hundred-Page ML Book – Concise, practical insights.
Awesome AI/ML Resources - Collection of best free resources.

Difficulty/Timeline: (Intermediate | 6-8 weeks)

Next steps: Once you grasp ML basics, move to building real-world AI projects.

4. Build Practical Experience

Hands-on projects are critical for landing a job in AI/ML. Start with:

Hands-On ML with Scikit-Learn, Keras, and TensorFlow – Practical guide to ML.
Practical Deep Learning for Coders – Hands-on deep learning course.
Structured ML Projects – Learn to structure and deploy models.
Build Your Own GPT – Build a small-scale GPT-like model.

Difficulty/Timeline: (Intermediate, ongoing)

Next steps: Once you’re comfortable building projects, learn about MLOps to deploy models at scale.

5. Learn About MLOps

MLOps is essential for deploying AI at scale. Learn:

Intro to MLOps – Fundamentals of MLOps.
Three Levels of ML Software – Best practices for production ML.
Full Stack Deep Learning – Full-cycle ML deployment.

Difficulty/Timeline: (Intermediate, 2-4 weeks)

Next steps: Once you’re comfortable with model deployment, niche down to AI domains.

6. Deepen Knowledge in Specialized Areas

Once comfortable with ML fundamentals, explore:

Natural Language Processing – Text-based AI.
Reinforcement Learning – Decision-making AI.
Computer Vision – Image-based AI.
Deep Learning – Advanced neural networks.
Transformers – Architecture behind ChatGPT.

Difficulty/Timeline: (Advanced, ongoing)

7. Stay Updated with AI Research

AI is evolving rapidly—stay ahead by following the latest research and developments.

ArXiv – The best place to find AI research papers.
Open AI Key Papers in Deep RL – A curated collection of must-read papers from OpenAI.

Difficulty/Timeline: (Advanced, ongoing)

8. Prepare for AI/ML Job Interviews

Landing an AI job requires you to pass some domain knowledge interviews. Study:

Intro to ML Interviews – Common ML interview questions.
Designing ML Systems – System design for AI.

Difficulty/Timeline: (Advanced, 4-6 weeks)

Next steps: Once you’ve prepped for interviews, now you’re ready to apply to jobs.

AI/ML Job Interviews

In my experience, AI/ML job interviews follow the standard software engineering interview format with a small twist—expect additional focus on machine learning domain knowledge and system design.

A typical AI/ML interview process includes:

Coding Interviews – Data structures and algorithms, similar to standard software engineering roles. Practice LeetCode and solve company-tagged questions.
System Design Round– Focuses on designing scalable, reliable, and efficient software systems. Expect questions on architecture, databases, caching, load balancing, concurrency, and distributed system. AI/ML roles may also include AI system design questions, such as designing a recommendation system or an ML pipeline.
Machine Learning Fundamentals – Questions on ML algorithms, model evaluation, bias/variance trade-offs, and optimization techniques.
Behavioral Interview – A resume walkthrough where you explain how you've applied AI/ML in real-world projects. Expect to discuss challenges faced, trade-offs made, and business impact.

For my interviews, loops typically included 2-3 coding interviews, 1 system design round, and 1 behavioral interview, with machine learning fundamentals integrated into each round. My experience collaborating with machine learning engineers and data scientists at TikTok was valuable, but in hindsight, I could have self-studied most of the required topics and still been well-prepared.

Interview Prep Plan

Now you’re ready to actually prep for interviews. Here is a 3 month prep plan I’d recommend.

Month 1: Strengthen Core Coding & Algorithm Skills

Goal: Ace the Coding Interview

Data Structures & Algorithms: Arrays, HashMaps, Graphs, Trees, Dynamic Programming
LeetCode Focus: Medium/Hard problems (start with company-tagged questions)
System Design Basics: Learn scalability, databases, caching, concurrency
Mock Interviews: Start doing 1-2 mock coding interviews per week

Resources:

NeetCode.io - Everything you need to pass the coding interview.
Interviewing.io - Mock interviews with target companies
Grokking the System Design Interview – Best intro to system design

Month 2: Master Machine Learning Fundamentals

Goal: Be able to explain ML concepts, solve ML problems, and discuss trade-offs.

Supervised vs. Unsupervised Learning
Overfitting, Regularization, Bias-Variance Tradeoff
Feature Engineering & Model Evaluation
Optimization Techniques: Hyperparameter tuning, Learning rates
ML Deployment & MLOps (CI/CD for ML, model versioning)

Resources:

Machine Learning by Andrew Ng (Coursera) – The best intro ML course
The Hundred-Page Machine Learning Book – Fastest way to cover ML concepts
Made with ML – Covers real-world ML workflows

Month 3: AI System Design & Real-World ML Problems

Goal: Learn AI-specific system design, prepare for ML modeling case studies, and refine interview strategy.

ML System Design: How to design AI applications like recommendation systems, fraud detection, NLP pipelines
Real-World ML Problems: Deployment, monitoring, scaling

Resources:

Designing Machine Learning Systems by Chip Huyen – Best AI system design book
ML Interviews by Chip Huyen – Focuses on practical ML interview questions

Recommended Courses

Learning AI/ML requires both theoretical knowledge and hands-on practice, and high-quality courses can accelerate your understanding. Below are some of the best AI/ML courses I recommend.

Machine Learning by Andrew Ng (Coursera) – A classic introduction to ML fundamentals, covering supervised and unsupervised learning.
Natural Language Processing with Deep Learning (Stanford - CS224n) – One of the best courses on NLP, covering transformers and large language models.
Deep Learning Specialization (Coursera) – In-depth coverage of deep learning, neural networks, and optimization techniques.
CS231n: Convolutional Neural Networks for Visual Recognition (Stanford) – A deep dive into CNNs, covering image classification and object detection.
Fast.ai’s Practical Deep Learning for Coders – A hands-on deep learning course focused on building models quickly with PyTorch.
Reinforcement Learning Course – Lectures by David Silver, a lead researcher at DeepMind, covering key RL algorithms.

Research Papers

I recommend staying up to date on key AI/ML research papers to understand the latest advancements.

You don’t need to grasp every technical detail—what matters is cultivating a genuine interest in the field.

Here are some essential papers that I find particularly interesting.

Attention Is All You Need (Google) – The original paper introducing Transformers, which power models like ChatGPT, BERT, and GPT-4.
DeepSeek R1: Incentivizing Reasoning Capability in LLMs – Recent work on improving logical reasoning in large language models.
Monolith: Real-Time Recommendation System (TikTok/ByteDance) – A look at how TikTok’s recommendation algorithm works at scale.
BERT: Pre-training of Deep Bidirectional Transformers – A deep dive into BERT, one of the first self-supervised NLP models that improved contextual understanding.
Distilling the Knowledge in a Neural Network – Introduces knowledge distillation, a key technique for training smaller, more efficient AI models.

Conclusion

Breaking into AI/ML may seem overwhelming, but it’s completely achievable with the right strategy.

Start small. Learn Python & core ML concepts.
Work on projects. Build real-world AI applications.
Prepare for interviews. Master coding, system design, and ML domain knowledge.

Thanks for reading,
Arman Khondker