Everything you need to know about hiring dedicated AI developers
First of all – To understand how software engineering will be changed by AI you have to look through the glasses of AI. We wrapped together the way from developing an understanding of AI in software development to outline what to look for when hiring an Artificial Intelligence engineer.
Simple Definition of AI
Artificial intelligence (AI) is the ability of a system to replicate human behavior. It deals with methods that enable a computer to solve such tasks that, when solved by humans, require a particular level of intelligence. Machines and therefore algorithms can simulate this kind of intelligence, which can ultimately be compared to the natural intelligence of humans.
Artificial intelligence today has many specialized uses, such as implementing expert systems, natural language processing, speech recognition, and using machine vision.
See this article from techtarget.com for further insights.
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What is AI Engineering?
Artificial intelligence engineering builds on systems engineering, software engineering, and computer science. It combines these disciplines to create artificial intelligence systems that can perform complex tasks, similar to how a human organism would do.
Tasks of an AI Engineer
An Artificial Intelligence Engineer can specialize in Machine Learning or Deep Learning. With Machine Learning the focus in AI software development is on decision paths/trees and algorithms on the one side. Neural networks play a role in Deep Learning on the other side. Deep Learning sets itself apart in the ability to process unstructured data through these neural networks. In the end both are extracting and processing data while developing needed AI systems to run flawlessly.
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Roles in AI Engineering
A short overview over the roles in the field of AI engineering
Grabs data from several different sources and uses a scientific approach with Artificial Intelligence or other Machine Learning methods and other advanced analysis and prediction techniques.
This type translates questions from an organization formulated via a business analyst into the data world. This role uses classical data analysis techniques.
Organizes and analyzes data sources and the infrastructure that these run on. The data engineer takes care that all data is available in an optimized and solid way.
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Skills of AI Engineers
As it is impossible to be specific with the skills needed for each AI profile we are summing up the
- Python, R, Java, and C++ are languages to look for
- Deeper knowledge in mathematics and statistics
- Skilled in algorithms
- legal implications of data processing and analyze ethical implications
- Frameworks like PyTorch & TensorFlow
- design, implementation and surveillance of big data infrastructure
but also watch out for the
- Ability to succeed in a teamwork environment
- Self organizational skills as well as management skills if needed
- Reliability in order to meet deadlines and deliverables
- Of course: Problem solving skills
Also settle on the right choice of project management for you.
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Embedded AI Developers
Given the skills required and the ever-increasing demand for AI talent, companies around the world are looking to offshore AI developers as an alternative to increasingly scarce AI software development resources in the local market. If your plan is to hire AI developers.
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Salaries of AI Engineers
According to glassdoor, the estimated salary for an AI expert could be well over $100,000 per year, if US prices are taken as an example. Considering the different skill levels such as junior, middle, senior and lead, the range is from almost $65,000 for a junior-level AI engineer to $70-80,000 for middle, $90-100,000 for senior and above for a lead-level AI developer.
More on the topic of the different skill levels of developers can you find here.
When you hire AI talent from the codecombinator ecosphere, you can save valuable resources, because we charge up to 40% less than the comparable domestic talent.
✅ See what a good AI software developer looks like
AI software development seems like the new kid on the block regarding the tremendous development steps we see recently. The big buzz going through the media is leading to shortened product cycles when AI systems are combined and automation comes into play in completely connected content production tools.
In the end AI and AI engineers are facing the same problems like every technology product. Garbage In – Garbage Out (GIGO) is a thing with artificial solutions as they do lack creativity and can produce results only on learned patterns.
The bigger the buzz is, the bigger is the gap between talent in need and offered AI tech talent on the other hand. Though it can be assumed that the wave of AI will lead to normality in dealing with the new solutions companies that are looking for resources will have a hard time finding them in domestic markets.
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