AI and ML are some of the fastest-growing domains in tech. The demand for AI Engineers and ML Engineers is increasing as more companies rely on automation and data-driven decision-making. However, it differs in areas of focus, skills required, and career paths; even though both roles work with intelligent systems.
Those who are aspiring to the field of AI, we suggest you to go for an artificial intelligence and machine learning course like the Purdue course for a solid foundation. This article will discuss the differences between an AI Engineer and an ML Engineer as well as their roles and responsibilities, skills needed, and growth prospects.
What is an AI Engineer?
An AI Engineer designs, develops, and manages AI models and applications that simulate human intelligence. Which specialized in developing collaborative systems that could reason, learn, and act autonomously.
1.1 Responsibilities of an AI Engineer
- Building AI powered applications like chatbots, recommendation systems, autonomous systems etc.
- Tackling with Machine Learning, Deep Learning and Natural Language Processing (NLP).
- Using computer vision for image and video recognition tasks
- Building AI models into Software Apps and cloud platforms
- And TensorFrame: TensorFlow, PyTorch and OpenAI’s GPT models.
1.2 Key Skills for AI Engineers
- Programming Languages: Python, Java, C++.
- Machine Learning & Deep Learning: Understanding of neural networks and reinforcement learning.
- NLP and Computer Vision: Techniques for text processing and image recognition.
- Cloud Computing: Experience with AWS, Azure, or Google Cloud.
- Big Data Technologies: Working with Hadoop, Spark, and NoSQL databases.
What is an ML Engineer?
A Machine Learning Engineer is a person who develops and deploys Machine Learning Models. Unlike AI Engineers, ML Engineers are more concerned with algorithms, statistical models, and automation to train models and improve them over time.
2.1 Responsibilities of an ML Engineer
- Building predictive models for fraud detection, recommendation engines, and speech recognition
- They are used for cleaning and preprocessing large datasets for machine learning.
- You can implement and optimize ML algorithms.
- MLOps (Machine Learning Operations) best practices for deploying models
- A/B Testing for Validating Model Performance
2.2 Key Skills for ML Engineers
- Programming Languages: Python, R, Java, and Scala.
- Machine Learning Algorithms: Regression, classification, clustering, and deep learning.
- Data Engineering: Experience with SQL, Spark, and ETL pipelines.
- Model Deployment: Using Docker, Kubernetes, and Flask APIs.
- Version Control & CI/CD: Git, Jenkins, and MLflow.
AI Engineer vs. ML Engineer: Key Differences
| Factor | AI Engineer | ML Engineer |
| Focus Area | AI applications and intelligent systems | Machine learning models and automation |
| Algorithms Used | Deep learning, NLP, reinforcement learning | Supervised, unsupervised, and reinforcement learning |
| Primary Goal | Creating AI-powered applications that can mimic human intelligence | Building and optimizing machine learning models |
| Tools & Frameworks | TensorFlow, PyTorch, OpenAI GPT, NLP libraries | Scikit-learn, Keras, MLflow, PyTorch |
| Deployment | AI models integrated into applications and cloud platforms | ML models deployed using APIs, cloud services, and MLOps |
| Industry Use Cases | Self-driving cars, robotics, virtual assistants, recommendation engines | Predictive analytics, fraud detection, customer segmentation |
Career Path for AI Engineers and ML Engineers
4.1 How to Become an AI Engineer
- Learn Programming & AI Fundamentals
- Enroll in an artificial intelligence and machine learning course such as the Purdue course.
- Gain expertise in Python, Java, and deep learning.
- Master AI Technologies
- Learn about NLP, computer vision, reinforcement learning.
- Work with frameworks like TensorFlow, PyTorch, and OpenCV.
- Build AI Projects
- Create AI-powered applications like chatbots, recommendation systems, and voice assistants.
- Showcase projects on GitHub and Kaggle.
- Gain Hands-On Experience
- Apply for internships or AI research roles.
- Work on AI-based projects in cloud environments (AWS, Azure, GCP).
- Get Certified & Apply for Jobs
- Earn AI certifications such as Google Professional Machine Learning Engineer.
- Apply for roles like AI Engineer, Deep Learning Engineer, or AI Researcher.
4.2 How to Become an ML Engineer
- Develop Strong Programming & Data Science Skills
- Learn Python, R, and SQL.
- Understand statistics, probability, and ML algorithms.
- Learn Machine Learning Techniques
- Study classification, regression, clustering, and deep learning.
- Use Scikit-learn, Keras, and TensorFlow.
- Work with Big Data & Cloud Platforms
- Gain experience with Hadoop, Spark, and NoSQL databases.
- Learn cloud-based ML deployment (AWS Sagemaker, Azure ML, GCP AI Platform).
- Build & Deploy ML Models
- Work on real-world projects such as fraud detection, sentiment analysis, and predictive analytics.
- Use Docker, Kubernetes, and CI/CD pipelines for deployment.
- Earn Certifications & Apply for Jobs
- Consider AWS Certified Machine Learning – Specialty certification.
- Apply for roles like Machine Learning Engineer, Data Scientist, or AI Specialist.
Salary Comparison: AI Engineer vs. ML Engineer
- AI Engineer Average Salary (2025 Estimates):
- USA: $120,000 – $160,000 per year
- India: ₹12-25 LPA
- UK: £70,000 – £110,000 per year
- ML Engineer Average Salary (2025 Estimates):
- USA: $100,000 – $140,000 per year
- India: ₹10-22 LPA
- UK: £60,000 – £100,000 per year
While both careers offer lucrative salaries, AI Engineers generally earn higher salaries due to their expertise in advanced AI models and deep learning techniques.
Which Career Should You Choose?
- Choose AI Engineering if:
- You are interested in creating intelligent systems and AI-powered applications.
- You enjoy working with NLP, deep learning, and computer vision.
- You want to work in fields like robotics, self-driving cars, and AI research.
- Choose ML Engineering if:
- You prefer working on statistical models and predictive analytics.
- You are interested in big data, cloud computing, and automation.
- You want to focus on deploying machine learning models in business applications.
Conclusion
In 2025, AI and ML engineering will be promising career quests. Suppose you want to up skill in years ahead; the best approach is to do a course in artificial intelligence with machine learning and a structured environment to learn first, i.e., the Purdue course.
Merely being aware of this will not suffice; by honing the appropriate abilities, tools, and technologies, you can develop a successful career as an AI Engineer or an ML Engineer, depending on the latter and the commercial need.
