The key rationale behind the employment of artificial intelligence is to enable machines or systems to act by themselves without repeated programmer coding.
Artificial intelligence is a broader concept which deals with embedding human intelligence into machines. It enables them to think, process, learn and correct themselves without any human intervention.
To make an aspiring career out of AI & ML, one needs to have in-depth knowledge of data science & statistics; and certain basic knowledge of data processing & software engineering. It is not just the technical know-how but also some of the soft skills that can make a difference.
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Also Read: Engineers Day: Top 5 in-demand skills for engineers
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A few of the important technical and non-technical skills required in the field of AI are the following:
Technical Skills:
- Computer programming – In-depth knowledge of programming fundamentals starting from computer architecture, optimization algorithms, data structures, trees, and graphs is required; This knowledge helps engineers to make AI simulate humans.
- Statistics & probability – Statistics is the root of data science; and an integral part of machine learning and artificial intelligence. AI engineers must be proficient in probability concepts including conditional probability, Markov models, Bayesian principles, etc.
- Data modelling – Knowledge of different types of data modelling techniques since they are extensively used in AI to handle pattern recognition & datasets classifications.
- Good Command Over Unix Tools – Since most of the processing in AI will happen in a Linux-based environment, programmers are required to be well-versed with different Unix tools, their functions and ways to utilize them.
- Efficiency In Distributed Computing – The majority of AI jobs require programmers to deal with large volumes of data, which cannot be processed effectively using a single machine. Hence, programmers need to have skills in distributed computing as this data will be equally distributed across systems.
- Design & software – Although this skill may seem the most irrelevant for an artificial intelligence engineer; one must have a fair understanding of system design & deployment as the final objective of their AI-driven project would be a developed software or an application.
Non-Technical Skills:
- Communication Skills – Not just AI but for any job description, communication skills are listed as a core requirement. With the technical know-how, one must be able to effectively communicate their ideas to the team. Miscommunication or lack of communication may lead to business loss due to revised project objectives, and extended timelines, driving more costs.
- Collaboration – AI requires one to understand neural networks and the behaviour of the human brain. This is not possible if engineers work in silos. Collaboration is an effective way of getting together and understanding different mindsets.
- Marketing Skills – Even if it is the most successful project one might have done, it would be a waste if not known by the world. Marketing yourself is as important as possessing those technical skills. One needs to understand that the end-user of the application would be a layman. Presenting a use-case where one has solved a pain-point of an end-user will grab more attention than explaining technical concepts.
- Systematic and Analytical Thinking – In order to make a successful career in AI, one needs to develop excellent problem-solving skills through systematic and analytical thinking. They need to have a curious mind to come up with innovative solutions for a problem. In addition, AI aspirants need to have a passion to keep themselves abreast with the latest developments and innovations in the technology industry. They must actively look for skilling opportunities to keep themselves industry-relevant.
AI and ML are the trending jobs lately and the learning of some of the skills mentioned above will definitely help the talent to find exciting opportunities and shape the future of technology.
