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Certain technical and non-technical skills are common for both AI and ML engineers.  

Artificial Intelligence (AI) and Machine Learning (ML) are two of the fastest-growing technologies pervading all sectors. From automated cars to chatbots, mobile phones, and other electronic devices, they have numerous applications. Consequently, the demand for AI and ML engineers with specific skills is growing rapidly. Certain technical and non-technical skills are common for both AI and ML engineers:

Technical skills

Programming languages: A strong grasp of programming languages such as Python, Java, R, and C++ is vital. These are easy to learn and tend to have a wider scope. Python, in fact, is considered the lingua franca of ML.

Linear algebra, Calculus, Statistics: One must have a thorough understanding of concepts such as Matrices, Vectors, Derivatives, and Integrals and a firm grasp of statistical concepts such as Mean, Standard Deviations, and Gaussian Distributions, along with probability theory for algorithms such as Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models.

Signal processing techniques: AI and ML engineers must know how to solve problems using Signal Processing. Additional knowledge of Advanced Signal Processing Algorithms such as Wavelets, Shearlets, Curvelets, and Bandlets is a bonus.

Applied Maths and algorithms: Apart form being well-versed in applied Maths, knowledge of algorithm theory can help in understanding crucial subjects such as Gradient Descent, Convex Optimisation, Lagrange, Quadratic Programming, Partial Differential Equations, and Summations.

Neural network architectures: Used for coding tasks that are arduous for human effort, this has been extremely useful in areas such as translation, speech recognition, and image classification, and so on.

Non-technical skills

Communication: Explaining complex topics to people who aren’t from the industry requires clear communication skills. Additionally, engineers often work in teams that include non-technical personnel from sales and marketing departments. Unless they can communicate the relevance of what they are working on, it will be tough for the product to gain traction in the market.

Domain expertise: Business owners expect industry-specific solutions from these emerging technologies. Therefore, AI/ML engineers must thoroughly understand the domain they will be working in. For example, creating AI or ML solutions for a genetic engineering firm requires a basic understanding of fundamental genetic engineering concepts.

Rapid prototyping: Launching products quickly in the market is every business’s goal today. Rapid prototyping helps form different techniques to develop a scale model and allows engineers to quickly develop a prototype and test it out.

Besides these, there are a few skills that are specific to Machine Learning engineers only. They are:

Natural Language Processing (NLP): It is a fundamental part of ML, and studies how machines understand and interpret human language. There are several libraries such as Gensim and NLTK that provide the NLP’S foundation and contain different functions to help computers understand our language. This is accomplished by breaking down the text according to its syntax, extracting important phrases, removing unnecessary words, and so on.

Reinforcement learning: It is the primary reason behind the sudden improvements in deep learning, and has the potential to revolutionise Robotics in the foreseeable future.

The growing demand for these technologies means that individuals who spend time learning these skills will be able to carve out a successful career.

The writer is the President – Judge India (Global Delivery) at The Judge Group.

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Printable version | Sep 22, 2021 2:06:04 AM |

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