Market demand for engineers and AI specialists has grown by leaps and bounds over the past five years and has been greatly accelerated by the Covid-19 pandemic. Most companies have realized that the use of AI tools and technologies decreases the need for human intervention and also results in cost savings. The pandemic has meant that human availability and interaction has been limited. These have pushed AI to become a field in its own right.
According to Maxima Group, there is a more than 75% increase in demand for AI specialists and engineers since the first quarter of 2020. But if the demand is high, why isn’t everyone picking up money? offer?
ETCIO spoke with technology leaders and recruiting agencies to understand how you, as an AI specialist, can improve your resume to land the right job for you.
Today, companies are looking for candidates with experience in NLP (Natural Language Processing) and machine and deep learning.
“Some experience with machine and deep learning libraries such as TensorFlow and PyTorch is also very positive. Technical experience in Python and SQL is required. Since most companies are migrating their IT infrastructure to the cloud, they prefer candidates with experience with AWS (Amazon Web Services), Microsoft Azure, or Google Cloud. These are all some of the critical DevOps, Cloud and CI / CD experiences that a potential AI specialist must have, ”said Suvarna Ghosh, Founding Partner, Maxima Group.
Although basic AI skills are essential, for example, knowledge of various statistical techniques and relevant AI algorithms, as well as experience in developing such solutions. But the most important aspect is the experience with the know-how of the domain.
“When building industrial AI applications, a major effort is devoted to streamlining data in the context of AI applications. A lot of development time is spent creating a common understanding between the domain expert and the data scientist. So it helps a lot to have good domain knowledge in addition to data science skill. It helps to set the context, connect the data to the context and understand all the relevant issues of reliability, explainability and risk, ”said Dr Shrikant Bhat, Senior Senior Scientist, India – Research Center of India. company, ABB Group.
The role of AI is to increase decision making for stakeholders. For human intelligence (to be used for decision making), most of the aspects inherent in interpreting context, relating data and actions to context are often implicit. However, by the very nature of artificial intelligence, this must be explicitly defined at each phase of the lifecycle of AI products / applications. Therefore, Bhat believes that if relevant AI experience is presented within this framework of the AI product lifecycle and its ability to increase decision making, it helps reflect the candidate’s basic understanding.
With the ever-changing technological landscape in today’s world, it is very important that AI specialists continue to hone, hone and perfect themselves continuously. Top AI and tech executives expect AI specialists to be familiar with programming languages like Python or R. Java, concepts of linear algebra and statistics, and network architectures. neural in the case of deep learning.
“It is important to constantly stay in touch with their field through activities such as open innovation challenges in AI, AI conferences, courses and certifications in the field. In addition, IBM’s AI certifications and certifications such as Microsoft Azure AI and AWS Machine Learning are extremely helpful, ”said Sindhu Ramachandran S, Managing Director and Leader – AI Center of Excellence, QuEST Global.
If you have previous experience, you should highlight your expertise in Vision Analytics, Data Analytics, and NLP. It would be beneficial to include details on the platforms (cloud, edge, embedded) used to deploy the AI solutions. Make sure you have listed all the courses and certifications you have taken in the field and include the link to your personal Github page, if applicable. Include details of documents and articles that you may have published.
Ramachandran thinks that, more importantly, for every AI project someone has worked on, be sure to mention the following about the project:
- Description of the use case, problem statement
- Type of data processed (Structures / Unstructured)
- Algorithms used
- POC or production solution
- Challenges encountered and how were these challenges resolved
- KPI
- Your contribution / role in the project