By Ezrah 
Becoming a successful AI engineer is not difficult, but you must have a clear idea of the roadmap to your goal. Career opportunities in the AI field are rapidly increasing, and so is the demand for AI engineers. Besides having work experience, an industry-recognised certification will boost your growth in the field.
For example, a Machine Learning Engineering Professional course can prepare you to become an AI engineer with machine learning skills. As businesses expand, the demand for AI engineers with machine learning skills is increasing.
Let’s start the discussion by understanding what AI engineers do and what their responsibilities are.
What does an AI Engineer Do?
Before you start enrolling in an AI engineer certificate course, you should understand the work responsibilities of an AI engineer. The work responsibilities of AI engineers involve applying knowledge of software engineering, data science, and machine learning. Using knowledge from all these fields, they prepare a working AI model. It also involves in-depth research on the field where the AI model will work.
When you become an AI engineer, you will have to collaborate with AI data scientists and software engineers. The following section will clarify how different professionals work in tandem to build a successful AI model.
- AI Engineers – They write Python code, employ machine learning frameworks, and build AI systems to integrate into real products.
- AI data scientists – Their job is to explore data and build models that can help an AI model work efficiently.
- Software Engineers – They design the infrastructure to make the end product fast, scalable, and stable.
If you want to work better with other professionals and develop AI models, you’d better pursue an AI ML certification. You not only get knowledge about AI and its application, but also get to know about machine learning.
Now Let’s see how you can get certifications in this field.
Certification Roadmap to Become an AI Engineer
You can start by increasing your understanding of AI and Machine Learning. The Machine Learning Engineering Professional (MLEP) by GIPMC can be a good starting point. Let’s see what the course offers.
Machine Learning Engineering Professional (MLEP) by GIPMC
Course Description: The certification is designed to validate and recognize in building, deploying, monitoring, and maintaining machine learning systems. The course can work as a foundation to become a successful AI engineer. Approximately 65-85% cannot reach the production level due to a lack of knowledge in machine learning.
Course Features: The course focuses on a production-grade machine learning system. Also emphasise scalability, reliability, and maintain the overall machine learning system.
Learning Outcome: In this AI ML certification course, you will learn
- The fundamentals of machine learning engineering
- Data Pipelines and Feature Engineering
- Model Training and Experiment Management
- Software Engineering for ML Systems
- Model Packaging and Deployment
- Infrastructure for Machine Learning
You can appear for the examination through online mode. The exam duration is 120 minutes, 120 MCQ-type questions will be asked, and it requires 70% to pass.
IBM AI Engineering Professional Certificate
There is another course offered by IBM that teaches all the necessary skills and knowledge required to become an AI engineer. Let’s take a look at the course details.
Course Description: The course is designed for data scientists, machine learning engineers, and software engineers. However, you can also enrol for the course if you are a technical professional looking to become an AI engineer.
Features: During this AI engineer certificate course, you will master concepts like machine learning and deep learning, supervised and unsupervised learning, and you will learn to use Python. Also, you will know how to apply popular libraries like SciPy, ScikitLearn, Keras, PyTorch, and TensorFlow to resolve industry problems.
Learning Outcome: The course includes several chapters:
- Machine Learning with Python
- Introduction to Deep Learning & Neural Networks with Keras
- Deep Learning with Keras and Tensorflow
- Introduction to Neural Networks and PyTorch
- Deep Learning with PyTorch
- AI Capstone Project with Deep Learning
Apart from these chapters, several others are included in the course on Generative AI, its development, and application in the real world issues. Finally, you will learn to build Generative AI applications using LLMs and RAG using frameworks like Hugging Face and LangChain.
Conclusion
If you want to be an AI engineer, better be one with the necessary knowledge in the field. You must know about machine learning, LLM, data science, and Generative AI. When you are working in a professional capacity, you are expected to collaborate with other professionals. Knowing other areas will also be helpful for you.
Ready to Become an AI Engineer? Choose a Certification Course
Now, if you are determined to become an AI engineer, you should start the journey today! Enrol in an AI ML certification course to become a professional in no time.