Advances in artificial intelligence (AI) have led to the creation of new platforms and tools that assist developers in building AI models and applications. Basic knowledge of these tools has also become important for hiring managers selecting a potential artificial intelligence developer. This guide provides hiring managers with an overview of assessing a candidate's skills based on their knowledge of AI tools and platforms.
AI developers with expertise in various tools and platforms are in high demand. Reports indicate that 0.07% of Canadian job postings seek generative AI developers, 17% target machine learning developers, and 5% look for data scientists. These statistics demonstrate a significant need among hiring managers for candidates with diverse experience in AI tools and platforms, reflecting the industry's growing emphasis on specialized skills.
Here are some tools and frameworks that hiring managers should be aware of when hiring suitable AI developers.
AI Frameworks and Libraries
AI frameworks and libraries are pre-built collections of tools and resources designed to streamline the creation of AI tools. These libraries are cost-effective for IT companies that are developing software applications. Here are some of the most commonly used frameworks and libraries that hiring managers should be aware of:
TensorFlow
Google developed the TensorFlow framework for deep learning and machine learning. This framework is used for model tracking, performance monitoring, data automation, and model training. TensorFlow is also used to develop models for tasks such as natural language processing, handwriting recognition, and image recognition.
Hiring managers should confirm a candidate's ability to design, train, and deploy models using TensorFlow before hiring an AI developer.
PyTorch
Facebook’s AI research lab developed PyTorch to make debugging easier and coding more natural. This library is ideal for its flexibility and ease of use when quickly prototyping neural networks. Hiring managers can assess candidates' ability to debug neural networks using Pytorch. Similarly, a candidate's contribution to open source projects with PyTorch is also a good point in their favour.
Programming Languages
Programming languages are a set of instructions written by programmers to instruct computers in performing a task. In terms of AI programming, coders use many different coding languages to develop applications. Here are some of the AI programming languages:
Python
Python is the most widely used programming language in AI development due to its readability, simplicity, and library support. It is also used to develop websites, software, data analysis, data visualization, and task automation.
Candidates with proficient Python skills can effectively utilize AI libraries and frameworks. Hiring managers can evaluate a candidate's Python skills through coding challenges and pair programming tasks.
Programming Language R
R is the most commonly used AI programming language for statistical data computing and data visualization. It is also effective in machine learning algorithms.
Hiring managers can provide practical data analysis and model-building tests to check their candidates’ proficiency in the R programming language.
In AI development, data processing tools are designed to collect, store, process, analyze, and present data in various formats. This helps to extract meaningful insights and make informed decisions. Some data processing tools hiring managers should be aware of include:
Apache Hadoop
Apache Hadoop is a framework that distributes the processing of large data sets across many computers using programming models. This tool is used to scale up data storage from gigabytes to petabytes. Hiring managers can set up Apache Hadoop to assess candidates' ability to process and analyze large amounts of data.
PandasAI
PandasAI is a Python library that uses generative AI models to interpret and understand natural language queries and translate them into SQL queries and Python code. This tool is mostly used to interact with data more naturally.
When hiring managers look for Artificial Intelligence developers to hire, they can test candidates to efficiently manipulate data using PandasAI. Experience in using PandasAI is also a positive when hiring AI developers.
Integrated Development Environments (IDEs)
Integrated Development Environments (IDEs) are software applications that help developers develop software code more efficiently. They are easy-to-use applications that help AI developers combine, edit, build, test, and pack software. Below are some tools used under IDEs.
Jupyter Notebook
In AI development, Jupyter Notebook is an open-source application that helps developers create and share live codes, equations, narrative text, and visualization documents. This is used in data science and for interactive and visual AI capabilities. Hiring managers can look for the AI developer’s ability to present and document the codes using Jupyter Notebook.
PyCharm
PyCharm provides a wide range of essential tools for Python development. It is used to analyze code, graphical debugging, integrate unit testing, and support web development frameworks. A proficient candidate can test and debug code using PyCharm to check code quality in Python. Hiring managers can evaluate potential candidates based on their use of the features of PyCharm.
Understanding AI tools and platforms significantly enhances a hiring manager’s ability to recruit top talent. Frameworks like TensorFlow and PyTorch, along with programming languages such as Python and R, offer unique capabilities that drive AI innovation. Data processing tools like Apache Hadoop and PandasAI, combined with IDEs like Jupyter Notebook and PyCharm, further refine the selection process. Hiring the right AI developer involves finding someone passionate, adaptable, and committed to continuous learning. With this knowledge, hiring managers can build a skilled and inspired team poised to push the boundaries of AI.
