The market demand for AI engineers and specialists has grown leaps and bounds in the last five years and was heavily accelerated by the Covid-19 pandemic. Most companies have realized that usage of AI tools and technologies lessens the need for human intervention and also results in cost efficiencies. The pandemic ensured that human availability and interaction have been limited. These have caused AI to mushroom as a domain by itself.According to Maxima Group, there is a more than 75% rise in the demand for AI specialists and engineers since the first quarter of 2020. But if the demand is high, why is everyone not landing an offer?
ETCIO spoke with technology leaders and recruitment agencies to understand how you, as an AI specialist, can level up your resume to land just the right job offer for yourself.
Today, Companies search for candidates who have experience in NLP (Natural language processing) and machine and deep learning.
“Some experience of machine and deep learning libraries such as TensorFlow and PyTorch is also a big positive. Technical experience in Python and SQL is mandatory. Since most companies are moving their IT infrastructure to the cloud, they prefer candidates who have experience of AWS (Amazon Web Services), Microsoft Azure or Google Cloud. These are all some of the critical DevOps, Cloud and CI/CD experience that a potential AI specialist must have,” said Suvarna Ghosh, Founder Partner, Maxima Group.
While basic AI skills are a must, for example, knowledge of various statistical techniques and relevant AI algorithms, and experience in developing such solutions. But the most important aspect is the experience with the domain knowhow.
“While building Industrial AI applications, major effort goes into rationalizing data into the context of AI applications. A lot of development time is spent in creating a common understanding between the domain expert and data scientist. So, it helps a lot to have good domain knowhow in addition to the data science skill. This helps in defining the context, relating data to the context, and understanding all relevant issues with respect to trustworthiness, explainability, and risk,” said Dr. Shrikant Bhat, Senior Principal Scientist, India – Corporate Research Center, ABB Group.
AI’s role is to augment decision-making for stakeholders. For human intelligence (to be used for decision making), most of the inherent aspects of interpreting the context, relating data and action to the context are often implicit. However, by the very nature of artificial intelligence, this has to be explicitly defined at every phase of the AI product/application lifecycle. Therefore, Bhat feels that if any AI-relevant experience is presented in this framework of the AI product lifecycle and its ability to augment decision making, it helps to reflect the basic understanding of the candidate.
With the ever-evolving technological landscape in today’s world, it is very important for AI specialists to constantly keep upgrading, skilling, and reskilling themselves. Senior AI and technology leaders expect AI specialists to be well versed in programming languages like Python or R. Java, concepts of linear algebra and statistics and neural network architectures in the case of deep learning.
“It is important to constantly stay in touch with their domain through activities like open innovation challenges in AI, AI conferences, courses, and certifications in the field. Additionally, certifications from IBM in AI and certifications like Microsoft Azure AI and AWS Machine Learning are extremely helpful,” explained Sindhu Ramachandran S, General Manager & Leader – AI Centre 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 about the platforms (cloud, edge, embedded) used for deploying the AI solutions. Make sure you have enumerated all the courses and certifications you have done in the field and include the link to your personal Github page, if applicable. Do include the details of the papers and articles that you may have published.
Ramachandran feels that most importantly, for each AI project that someone has worked on, make sure to mention the following about the project:
- Description of the use case, problem statement
- Type of data handled (Structures/Unstructured)
- Algorithms used
- POC or Production Solution
- Challenges faced and how were those challenges were resolved
- KPIs
- Your contribution/role in the project
