Artificial intelligence has improved the potential of various businesses around the world and also contributed to those sectors that were earlier unaffected by this technology. Given the blossoming prospects of artificial intelligence, a large number of engineering colleges in India have started full-fledged undergraduate and postgraduate courses in this field. AI Engineering is becoming a very popular branch in the engineering institutions that are operating within the peripheries of the national capital. For instance, Ghaziabad colleges for B.Tech are now prioritizing short-term and long-term AI courses. Let us take a look at the career prospects of AI engineering and the skills required to become an AI engineer.
AI Engineering: The intriguing field
The main job of an AI engineer is to use machine learning algorithms for deriving various types of business insights. These business insights can prove critical for the decision-making process of an organization. AI engineers are proficient in the fields of programming, big data analytics as well as data science. This means that they can simultaneously work on different projects that are interdisciplinary in nature. Other important tools that are available to AI engineers include linear regression, logistic regression, time series forecasting, and the like.
In addition to the above-mentioned skills, AI engineers are proficient in application programming interfaces. It needs to be noted at this point in time that the application programming interface acts as a connecting link between the user and the computing interface. Such professionals can also help in the fabrication of AI models that are designed to achieve specific tasks. The fabrication of data transformation infrastructure also comes with that incircle of AI engineers. AI product development, life cycle modeling of a product as well as its maintenance is a part and parcel of the work performed by AI engineers.
The skillset of AI engineers
The skillset of AI engineers is very diverse. It includes programming skills in languages like Python, Java, C++. In addition to this, the knowledge of linear algebra, probability, and statistics is extremely important for AI engineers. AI engineers are proficient in developing various types of Markov models and Gaussian mixture models. They also have a detailed knowledge of linear discriminant analysis and are proficient in dimensionality reduction techniques for solving complex problems. AI engineers are trained to work with big data technologies and have knowledge of Hadoop as well as Cassandra. In addition to the above-mentioned skills of AI engineers, communication skills hold the key to sharing ideas with the client. After driving sufficient insights and solving a specific business problem, it is important that they communicate their ideas flawlessly. This is where their proficient communication skills come into action.
AI professionals
AI engineers work in various types of fields and consequently, their expertise differs in a significant manner. AI developers work closely with tools that are involved in the creation of intelligent robots. Similarly, AI architects provide business solutions for the integration of different types of business services as well as maintenance of architecture. Machine learning engineers are involved in the development of models that can carry out predictive analytics using voluminous data sets. They rely on machine learning algorithms as well as deep learning algorithms as their most important tools. The most diverse skill set is possessed by data scientists who process huge data sets and interpret information in a precise way. Business intelligence developers are involved in the design and development of such models that can forecast business trends and predict market dynamics.
The way ahead
In order to become an AI engineer, it is necessary to work with data products as well as predictive modeling methods. For this, various types of courses are available and some of them include artificial intelligence, data science, machine learning, deep learning, and AI capstone project. These courses act as a virtual passport for entering the job market and gaining the necessary skills that are aligned to modern jobs. The presence of both online and offline modules for gaining the necessary skills is a development that benefits thousands of aspirants who are desirous of a career in artificial intelligence and machine learning.
Concluding remarks
The need of the hour is to incorporate modern-day courses like artificial intelligence, machine learning, data science, big data, and analytics into our engineering curriculum so that the skill gap between industries and academics is bridged at the earliest.
