Given the developments in artificial intelligence in recent years, it is not unusual that many of those focused on IT professional careers and software developer careers are choosing AI Engineering careers and roles as AI experts.
For instance, the $422.37 billion global artificial intelligence (AI) market is expected to grow at a 39.4% CAGR between 2022 and 2028. These are outstanding numbers considering the average growth of the rest of the industries in the market.
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 software programming and development background, you can focus on areas beyond developer careers and start a lucrative AI Engineering career.
To begin, consider the following:
How to transition to an AI Expert career
For many IT professionals, a role in AI is a reasonable next step that takes advantage of some of their existing skills. Given the machine-learning packages that are built-in to Python and R out-of-the-box, programmers and developers who use those languages are able to easily upgrade their skills to become data scientists.
The skills of IT architects and DevOps specialists are especially well suited for data engineer positions, where they are responsible for building and overseeing the enormous data pipelines that enable machine learning. Even certain knowledge workers and domain specialists, such as those with expertise in data capture, information security, compliance, contracting, revenue operations, or operational efficiency, take advantage of possibilities to work on multi-functional AI project teams.
How do you start preparing? If you’re trying to transfer into an AI expert or AI Engineering careers, take into account these actions:
Solidify your Software Knowledge
First and foremost, one needs to be a skilled programmer with everything that entails writing well-engineered code, debugging when issues arise and having excellent data management abilities to succeed in a AI Engineering career. Learn Python if you’re unfamiliar with it.
Learn AI Basics
It’s crucial to gain a solid understanding of machine learning’s and artificial intelligence’s foundational algorithms and the kinds of solutions there are. With your current employer, volunteer for efforts that allow you to work on your own time to complete needed work.
Know your Field
Learn everything about the field in which you wish to use AI, if you aren’t already deeply involved. In fact, the most critical skill to emphasize is understanding what the business question is that has to be answered with machine learning. This requires listening skills, knowledge of the field, and knowledge of how businesses operate in general.
Look for data science courses with an AI focus
Continuing education is a simple way to develop the abilities required for the machine learning and AI Engineering careers. Along with formal, effective data science programs, there are also a great deal of good, cost-free alternatives. Therefore, never stop training yourself.
In terms of getting certified, there is no better way to do it than with one of the cloud platforms. Such as Google Cloud, AWS, or Azure. After completing the basic courses, start looking for projects where you are able to join as a data analyst or business analyst and try to use your learnings while you’re upskilling. Thereupon, the best method of learning is through on-the-job training.
You need to carry out certain duties as an AI or ML engineer. Including creating, testing, and deploying AI models using coding techniques like random forest, logistic regression, linear regression, and others.
Among the responsibilities of an AI expert job are:
- Create application program interfaces (APIs) for the machine learning models so that other applications are able to use them
- Make AI models from scratch while assisting the various organizational constituents (such as product managers and stakeholders)
- Design infrastructure for data ingestion and data transformation
- Automate the data science team’s infrastructure
- Adjust the outcomes of the statistical analysis so that the organization makes more informed decisions
- Set up infrastructure for developing and managing AI products
- Perform administrative tasks like keeping an eye on the price of the resources used to create these AI systems. Additionally, they set up tools and systems in accordance with the specifications they developed.
In conclusion, whether you aspire to be a data scientist, ML engineer, BI developer, etc. its clear responsibilities and steps are similar. Let us tell you it is perfectly achievable. Building a career from the ground up in AI is a choice you won’t regret.
About The Brixton Group
Founded in 1998, The Brixton Group Inc. is a values-based, technology staffing and project solutions company. Our mission is to provide unparalleled service to our clients, consultants, and colleagues through our focus on long-term satisfaction. For more information, visit www.brixton.net