AI Developers Hiring Guide

Artificial Intelligence isn't just for science fiction

The singularity is not upon us, so you can put aside your concerns that the machines will soon be taking over. Quite the opposite. The machines are still, very much working for us. And thanks to Artificial Intelligence (AI), those machines not only work for us, but they also work smarter.

The very definition of Artificial Intelligence is intelligence demonstrated by machines. But AI is an umbrella for several different technologies (such as Machine Learning and Deep Learning) that come together to make machines capable of sensing, comprehending, acting, and learning with human-like intelligence.

Alan Turing’s paper “Computing Machinery and Intelligence” was published in 1950 and is considered the thesis which launched the idea of Artificial Intelligence. In that paper, Turing posed the question “Can machines think?” This paper also introduced the Imitation Game which is played by a man, a woman, and an interrogator. That game served as the basis for the Turing Test, which is a method of inquiry in AI to determine if a computer is capable of thinking like a human being.

No matter how you define it, AI has become an incredibly important technology for modern businesses. With AI companies can become more agile, competitive, and better capable of meeting an ever-growing demand.

Hiring Guide
Interview Questions
Job Description

Introduction

If you still don't believe Artificial Intelligence might be right for your business, consider these benefits. AI can:

Help eliminate friction within your organization
Improve analytics.
Reduce costs.
Improve accuracy and decision-making.
Uncover gaps and market opportunities.
Empower employees.
Improve customer service.
Reduce risk.
Assist in repetitive jobs.
Make faster decisions.

That shortlist alone should have you champing at the bit to integrate AI into your business.

But how? How can you integrate Artificial Intelligence into your business? It's not as hard as you might think. Yes, you're going to need developers adept at using the required tools to build AI, but once you've hired those developers, they can easily deploy AI in chatbots, big data synthesizers, smart algorithms to assist with audience-building, A/B testing, programmatic ad buying, and listening tools. All of those options can be deployed without nearly the effort you might think.

But before you can get to that point, you need to hire AI developers.

The Skills You Need

First and foremost, your developers need to know the right programming languages that are best suited for AI. Languages like Java, Scala, and Python are a must.

Those developers should also have an understanding of deep learning platforms (such as H20.AI) and deep learning libraries as well as Google's ML Kit. A solid understanding of how to work with and implement cloud platforms (such as Google Cloud, AWS, and Azure) is also critical.

There are plenty of APIs (such as OpenGL and PhysX) that will be required. Finally, your developers will need to have a deep understanding of how to use the popular frameworks associated with AI, which includes:

The skills necessary for the development of AI go beyond software. Your developers will also need to have a solid understanding of math and algorithms. AI developers must be strong problem solvers and math will be a crucial aspect of that skill. In that same vein, they'll need to have a solid grasp of probability and statistics. Why? Because AI is built on searching for predictable patterns and trends. Every AI developer should be well-versed in statistical methods and probability theories.

It's also important that your AI engineers be comfortable working with data. Without data, AI can’t learn. For AI to be successful, it will also need to pull data from multiple sources, so those developers must be able to work with numerous data collection methods.

Another skill you might not have considered is signal processing techniques, which are crucial for feature extraction. This also includes Time-frequency Analysis and Advanced Signal Processing algorithms like Wavelets, Shearlets, Curvelets, and Bandlets.

Needless to say, being a developer in the field of AI is much more involved and complex than standard application development.

AI Ethics

Another issue that must be considered, when hiring developers for AI is ethics. If your company is to dive into the realm of AI, you must keep ethics front and center. Why? Because you'll be deploying applications in such a way that must allow companies to earn and retain a level of trust with customers. Those Artificial Intelligence apps will be using consumer data in such a way as to empower your company. This requires trust.

For such trust, you'll need to make sure you're hiring developers you can rely on, who can deliver on data security, who are capable of working with transparency, and who can help develop risk frameworks and contingency plans, should problems arise.

Conclusion

The world of AI is only going to continue to grow on a massive scale. Your company needs to start working immediately to hire developers capable of working with this technology. To do that, you'll have to exercise a bit of care and caution to ensure you bring into your team's software engineers that have all the necessary requirements to get the job done right.

What is the difference between AI, Machine Learning, and Deep Learning?

AI represents simulated intelligence in a machine, ML is the practice of training machines to make decisions without programming, and DL is the process of using artificial neural networks to solve complex problems.

Give an example of how AI can be used in a business setting.

One of the most widely-used examples of AI is Google's search engine which uses predictive analysis to offer recommendations for users to select from.

What are the 7 types of AI?

Reactive Machines AI
Limited Memory AI
Theory of Mind AI
Self Aware AI
Artificial Narrow Intelligence
Artificial General Intelligence
Artificial Superhuman Intelligence

What are the 6 domains in AI?

Machine Learning
Neural Networks
Robotics
Expert Systems
Fuzzy Logic Systems
Natural Language Processing

What are the 3 types of Machine Learning?

Supervised Learning
Unsupervised Learning
Reinforcement Learning

What are the 3 types of layers in a deep neural network?

Input layer
Hidden layer
Output layer

What are Bayesian Networks?

A Bayesian Network is a statistical model that represents a set of variables and their associated conditional dependencies by way of a directed acyclic graph.

What are the 3 different algorithms used for hyperparameter optimization?

Grid Search
Random Search
Bayesian Optimization

Your job will be to develop AI software that can be integrated into our business systems. You will work with a team to help build and execute our AI strategy and aid with the AI development and production infrastructure, as well as create AI models and deploy them to production.

Responsibilities

Design and manage our AI development and production infrastructure.
Help all product managers and business stakeholders to understand the potential and limitations surrounding AI.
Build the necessary structure to manage and transform data for AI.
Identify new learning opportunities and training datasets.
Design and build AI models and help stakeholders understand the results.
Deploy AI models into production.
Develop APIs for AI production and integration.
Keep current on all the latest AI research relevant to our business domain.

Skills and Qualifications

5+ years of hands-on programming experience working on enterprise AI products.
Proficiency in multiple programming languages with a strong foundation in a statistical platform such as Python, R, SAS, or MatLab.
Experience building AI models and working with frameworks and platforms like TensorFlow or Theano.
Demonstrated commitment to learning about AI through your own initiatives through courses, books, or side projects.
Superior analytical skills with a good problem-solving attitude.
Fundamental understanding of version control systems (such as Git).
Solid problem-solving skills.
Excellent written and verbal communication.
Good organizational skills.
Ability to work as part of a team.
Attention to detail.
Understanding the nature of asynchronous programming and its quirks and workarounds
A positive attitude.