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.
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
thanks man can add this topic on youtube where it guides us in a form of short video i think we help to funel
Ok yes I subscribed 👏👍