AI engineers are in high demand and for good reason. Artificial intelligence has seemingly endless potential to improve and simplify tasks commonly done by humans, including speech recognition, image processing, business process management, and even the diagnosis of disease. If you’re already technically inclined and have a background in software programming, you may want to consider a lucrative AI career and know about how to become an AI engineer.
- What do AI engineers do?
AI or machine learning engineers work on a variety of projects. Depending on the role, the main responsibilities of the job could be:
- Computer programming, using languages such as Java, Python, or C#
- Working with data, statistics, and algorithms
- Using data modeling and evaluation strategies
- Applying machine learning algorithms and libraries
- Research and design deep learning applications
There are many other things that AI engineers may work on, many will be specific to the project. For instance, if you’re working on self-driving cars there could be elements of computer vision, creating deep learning models, sensor fusion, and filtering and programming.
The role of an AI engineer can be broken down into 4 categories :
- AI or Machine Learning Researcher - This role involves exploring the theoretical side of AI and looking to further the development of the technology or apply it to new areas. They are often Masters or Ph.D. educated.
- AI Software Developer or Program Manager - These engineers apply machine learning to a given data set. Strong programming and mathematical skills are needed for this role.
- Data Analysis and Data Mining Engineers - Modelling and creating deep-learning systems to recognize and respond to patterns.
- Machine Learning Engineers - This covers everything else AI-related - i.e. industry-specific machine learning applications. In other words, using and applying AI techniques to perform functions or solve problems in a business setting.
- Types of Artificial Intelligence
- Purely Reactive: These AI machines start with no data or memory and are built for accomplishing specific tasks such as playing chess.
- Limited Memory: These AI machines have a minimal amount of memory to generate decisions for an instant, like suggesting a restaurant in the neighborhood.
- Theory of Mind: This AI is predicted to work in the future to understand emotions and thoughts to interact socially.
- Self-Aware: Another projected AI machine with self-awareness capabilities to make conscious decisions.
- Roles and Responsibilities of an AI Engineer
AI Engineers have the responsibility to build AI models using Machine learning algorithms and neural networks to perform specific tasks to collect, process, and draw insights from data in complementing them with future decision making. AI Engineers are expected to have comprehensive knowledge in working with different programming languages, data science, and software engineering.
Here are the main roles AI engineers must perform in an organization to help them achieve their goals and objectives:
- Develop AI models from scratch
- Data modeling
- Automated infrastructure
- Perform statistical analysis
- Transform Machine learning models into Application Program interfaces (API)
- Coordinate with the team to bring together ideas for a more engaging work environment
- Set up and manage AI infrastructure
- AI Engineer Skills
To be a successful AI Engineer, you must have the skills to offer organizations the right path ahead and optimized solutions. The main skills professionals must acquire are:
- Programming Skills: Professionals must learn to program with Python, Java, R, C++, etc., to develop and implement models.
- Linear Algebra, Probability, and Statistics: Several complex AI models such as Naive Bayes, Hidden Markov, linear discriminant analysis, and Gaussian Mixture models are based on different aspects of statistics, probability, and linear algebra.
- Spark and Big Data Technologies: To handle a large amount of data from a real-time application, professionals must know their way around Spark and Big data technologies such as Hadoop, Apache Spark, MongoDB, and Cassandra.
- Algorithms and Frameworks: Machine learning and Deep learning algorithms are used to implement AI models, so professionals must understand these algorithms and frameworks such as TensorFlow, Thea-no, Caffe, and PyTorch.
- Communication and Problem-solving Abilities: AI engineers must have effective communication and problem-solving skills to present their ideas precisely and simplify obstacles to generate actionable insights from models.
AI engineers also need to familiarize themselves with trending skills for embracing the best practices in AI infrastructure. Thus, improving the efficiency of the system to build a more productive environment. This Artificial Intelligence tutorial can help you master these skills and prepare candidates for the industry level.
Steps to Becoming an AI Engineer
1. Gain Qualifications
Gaining the right qualifications is an important part of the journey to becoming an AI engineer. The first decision is whether to study for a college degree or not and if so, which course to take.
The best way to decide whether to study for an undergraduate degree in computer science or an AI-related subject is to ask yourself whether you want to be involved in research and development or not. If the answer is yes, then mathematics, computer science, or dedicated Artificial Intelligence degree, followed by a masters or Ph.D. in machine learning is recommended.
If you’re not interested in the research side of things and want to be more hands-on with the application of AI, then you don’t necessarily need a degree. College degrees have their limitations, especially in an emerging field like AI, as the stuff you’ll learn is already out of date by the time the curriculum is taught.
2. Develop Skills and Knowledge
You will need to develop both technical and personal skills to be a successful AI engineer. Some of the technical skills you should develop are:
- Programming - at least one of the following: Python, Java, C#
- Artificial Intelligence theory and techniques
- NLP and deep-learning
- Data Science applications
- Computer vision
The soft skills you should develop are:
- Problem-solving and analytical thinking
- Patience and resilience
- Working independently or as part of a team
- Attention to detail
- Focus
- Learn new skills and acquire new knowledge
3. Build a Portfolio of Work Experience
Because AI is an emerging technology, employers aren’t necessarily interested in formal degree qualifications. What they are looking for is a blend of online training, reading around the subject, and hands-on experience.
The more experience you can demonstrate, the more likely you are to land a well-paid AI engineer job.
Salary and Employ-ability
The big picture is very good for AI engineers, both in terms of job opportunities and potential earnings.
According to Indeed.com, the average salary for an AI or Machine Learning Engineer is $140,866, placing it as one of the highest-paid technical professions you can get at the moment.
Employer demand for AI engineers has skyrocketed over the past 3 years, with 2018’s growth rate at a massive 344%!
If you have the ability and you’re willing to learn the technical know-how, now is the time to become an AI engineer. There are fast-growing demand and a shortage of skills, making it one of the safest bets for a long and prosperous career.
Start your Exciting Journey to Becoming an AI Engineer
Don’t delay! Now’s the time to get the skills you need to get a career in one of the most in-demand jobs at the moment, and for the foreseeable future.
BEST OF LUCK...