If you’ve ever used Alexa or Siri, sat in a self-driving car, talked to a chatbot, or even watched something recommended to you by Netflix, you’ve come across Artificial Intelligence, or AI, as it’s commonly known.
AI appears to be the driving force behind the world’s advancement, in almost every field of study including healthcare, finance, entertainment, and transport. Simply put, Artificial Intelligence is the capability of machines to learn like humans, take problem-solving decisions and complete tasks that would otherwise require multiple individuals to invest long working hours.
We’re here to answer all your AI-related questions, so you can get humanity one step closer to planting Elon Musk’s neuralink chips into our brains and curing blindness! Or, just make a chatbot.
What is an AI engineer?
AI engineers create, repair, implement and improve artificial intelligence for organisations to use. They work with neural networks, build their machine learning models, test and implement existing AI models, use data science, act as problem-solvers for artificial intelligence technology and keep the organisation’s AI infrastructure running. And that’s not even all of it. AI engineers have excellent developer skills. A lot of their work is analysing data which informs the implementation of machine learning programs that automate tasks to improve efficiency.
The modern need of AI engineers and developers
In recent times, the industry has seen an enormous boom in hiring rates of AI engineers, and more developers are considering entering the field, which is probably why you’re reading this. Linkedin, Gartner and Forbes have all claimed AI specialists as one of the top emerging jobs in recent times, with LinkedIn giving Artificial Intelligence Specialist the number one spot in their ‘emerging jobs report’ for 2020!
An AI engineer’s day-to-day responsibilities
Chances are you won’t be hired by Lexcorp to make the next war machine that takes over the world (or so we hope). But in any AI engineering job, there are many daily tasks which jump between technical and some non-technical aspects:
Develop and execute useful AI applications and models
The core of an AI engineer's responsibilities is to create useful AI applications and machine learning models that complete specific tasks. The end goal often revolves around automating certain tasks so humans can focus their attention on more demanding projects.
Test AI experiments
Artificial Intelligence engineers continuously perform Machine learning/AI experiments with the right AI tools, and their machine learning algorithms are usually applied in new areas. This is why a disciplined work ethic and foresight are often required in such a responsible role.
Train junior developers on AI practices
The wealth of knowledge AI engineers gain over the years ranges from mastery in multiple programming languages to impeccable statistical and maths skills. Training new developers on how they should go about entering the field of AI and what would most benefit them would greatly help the field of AI, since it’s still an infant technology. Also, this makes delegation easier so you can enjoy your coffee in peace.
Keep up-to-date on AI advancements
We don’t need to tell you how rapidly things change in the world of technology. So, it becomes extremely important that as an AI engineer, you have first-hand knowledge of any new advancements that might change the game.
Work with engineers to bring your idea to life
As an AI engineer, you’ll have to work closely with the robotics team, electrical engineers and even software engineers to properly implement your projects to business applications and keep them running.
Maintain the organisation’s AI infrastructure
It would be your duty to ensure the existing machine learning algorithms are at optimal utilisation. Therefore, you’ll need to undergo training to keep up with the ever-changing technology.
Data mining and pattern recognition
Experienced AI engineers will tell you about their hands-on experience with collecting and analysing data, and selecting the appropriate data representation methods.
Pattern recognition like analysing objects in images or voice recognition with the help of machine learning algorithms is already widely used. Researching new uses of this technology and implementing them is something AI engineers are constantly working on.
Requirements for applying for an AI engineering role
Now that we’ve seen what they do, there are a few qualifications that will help you reach there. Here’s how you could start.
To set the groundwork, you’re going to need a bachelor’s degree in computer science, statistics, economics, finance, IT or mathematics.
Advanced degree or education: A Masters or industry certifications are recommended to aspiring AI engineers since specialisation in various fields like machine learning, programming languages, statistics and mathematics will be vital before you can claim the position.
Experience with AI models: As is the case in most professions, you need the experience to be able to gain the trust of your employers. AI is rapidly changing, and just theoretical knowledge without any practical experience might not put you on top of the list for hiring. It’s important to build a portfolio, mentioning any freelance work and personal projects you might have taken up, which will make it easier for potential employers to trust your capabilities.
A deep understanding of Natural Language Processing: Natural language processing is at the core of AI as we know it. Simply put, NLP refers to the programming of computers to analyse large sets of the natural language spoken by humans in a beneficial manner. It is a must for any AI engineer to be familiar with NLP tools and libraries like NTLK, Sentiment analytics, Gensim, etc.
Eight skills you’ll need to become an AI engineer
AI engineers conduct a variety of tasks that would fly right over the layman’s head. In fairness, creating and implementing machine learning algorithms sounds like something right out of a sci-fi movie. To be able to do that though, here are the skills every AI engineer must have:
To be able to create deep-learning models that analyse patterns, a strong understanding of analytics is a prerequisite. Being very grounded with analytics will help in testing and configuring AI.
2. Applied Mathematics
We’re guessing if you have an interest in Artificial Intelligence engineering, you probably don’t hate maths, since it is at the core of all things AI. firm understanding of gradient descent, quadratic programming and stuff like convex optimisation is necessary.
3. Statistics and algorithms
An adequate understanding of statistics is required while working with algorithms. AI engineers need to be well-versed in topics like standard deviation, probability, and models like Hidden Markov and Naive Bayes.
4. Language fluency
Yep, no surprises here. You’ll need to be fluent in a couple of languages to be a successful AI engineer. The most popular language amongst AI engineers is Python, but it often turns out to be too little on its own. It’s important to have proficiency in multiple languages like C, C++ and Java.
5. Problem-solving and communication skills
You’ll be forced to think out of the box. You’ll find there’s no set of rules or go-to guidelines you can adhere to if you’re ever in a pickle. AI often requires innovative use of machine learning models and creative thinking. You’ll also need to be able to communicate these ideas to your co-workers who may not have enough knowledge on the subject effectively in order to keep their trust.
6. Neural Network knowledge
Another important skill you’re going to need is efficiency with neural networks. A neural network is a software that works similarly to a human brain, helping in pattern recognition, solving complex problems and conducting image classification, which is a massive part of how we use AI.AI engineers often spend a lot of time working with neural networks; thus, a good understanding of the subject is required.
7. Team management
You’ll likely work independently. However, some aspects allow you to communicate with humans, too, instead of just machines. As an AI engineer, you will need to communicate your ideas with numerous other engineers and developers. Therefore, communication skills and management of employees come in handy while trying to work on a project. So while you’re solving math equations to prepare for your career, make sure you do it with people around you.
8. Cloud knowledge
Out of the many tricks AI engineers need to have under their belt, having a fair idea of what cloud architecture is, is right up there. Cloud architecture involves much more than just managing storage space, and knowing the difference between which secure storage system is best suited to your project will be extremely helpful.
The average AI engineer salary in 2020
The salary an AI engineer makes depends on experience, certification, and the location in which they’re working, but generally, AI engineers get paid well. According to Glassdoor, the average salary for AI engineers in the US is $114,121 per year as of 2020. Other sources claim the salary goes as high as $248,625 for experienced AI engineers. It sounds like you’ll be able to afford your dream house in Silicon Valley in no time.
As an AI engineer, your job is not monotonous by any means. New challenges and opportunities for innovative implementations of AI await them every day. The demands and skills needed may seem intimidating, but the reward and compensation make it all worth it. You might just end up significantly adding to the field of artificial intelligence and shape the way your organisation conducts its business.