The skilled professional of Artificial intelligence (AI) develop expert programs that enable computers to make judgments and provide guidance similar to the natural ability and expertise of humans. These expert programs perform the highly efficient computational capabilities in the areas of diagnosing illnesses, evaluating voluminous data, locating natural resources, and so on.
The Artificial intelligence professionals primarily work for the research centers of universities, start-up AI development companies, and of course the large corporations that have their own in-house AI units. Large corporations develop their own knowledge base on AI for their internal consumption whereas other companies may develop and sell instructional software created by AI professionals.
The industry has already been expecting AI programmers, who are very passionate and possess the in-depth understanding of fundamentals like data structures, strong problem-solving and algorithmic thinking skills. They should be able to write highly scalable, modular and performance optimized code. It is essential that programmers will have to gain good hands-on expertise in the following skillset:
No AI projects may have a pre-fixed technology landscape or architecture, as the current ideas, trends, and concepts would mature on a continuous basis. These concepts mature through the project lifecycle – ranging from scientific research, the creation of POCs, until the successful delivery of real-world applications.
So, AI professionals must work in multi-disciplinary project teams who can collaborate with functional subject matter experts, platform engineers, analytical team members, and technology development and delivery teams to assess the current AI capabilities, and develop the required customer use cases.
Solution design is one of the key steps that defines how insights from data are derived based on the application of analytics, data science and advanced cognitive methods, and helps in developing POCs and appropriate high-level solution designs.
Related Article: Reasons why you should learn Artificial Intelligence
Here is a brief idea on a few key AI job roles…
The AI consultants must have practical industry expertise. Besides possessing business expertise in one of the functional areas like financial services, retail, telecommunications, life sciences, and mining, they must be up-to-date with the key technology trends in their domain and the related business implications.
The ML/NLP research Engineers are expected to develop the server-side logic, write algorithms with mathematical models in areas of NLP, manage AI & ML/NLP Product hacker, develop the API/SDK (SaaS) based B2B Enterprise products, define and maintain various databases, integrate with the front-end elements, and ensure high performance and responsiveness to requests from the front-end.
The Robotics Process Automation Architects design an automation strategy and execute the delivery plan. They evaluate RPA/Artificial Intelligence (AI) tools, assess automation opportunities using AI technologies from the technical perspective and perform due diligence to arrive at an optimal solution. In addition, they define a technical framework based on Security, Method of access (internal client network, internet, local access), User Authentication (SSO, central LDAP, individual login), IT Security.
Up-skilling in AI now will help you gain job assurance in future. The future software engineer jobs may involve working with neural networks, human-machine interfaces, and quantum artificial intelligence. Or, they may include developing shopping list recommendation engines, or analyzing and processing big data. They may even include developing electronic parking assistants or home assistant robots.
Nobody thought about such jobs 10 years ago! They exist now, but the interesting fact is that the current jobs may even evolve much further and may look completely different in the next 10 years!!!
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Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.