An AI specialist is responsible for designing intelligent algorithms and models and using AI techniques to solve real-life problems. These experts have knowledge in tons of AI areas such as machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision. Read on to find out more about the role of an AI specialist.
- Who is the AI specialist?
- Demand for AI experts
- Skills required in AI
- Professional profiles in AI
- Education & background
- AI specialist salary
Job profile of an AI expert
AI has grown significantly in the past couple of years. From manufacturing to healthcare, AI has been integrated across most industries and is now starting to become an integral part of how we live our lives.
However, with this growth there comes a new challenge – a need for trained professionals who understand exactly how it works and to help organisations implement it all. This is where AI experts come in.
AI specialists are familiar with major AI technologies and platforms and work on systems that gather information, make decisions, and act on it.
In order to do this, AI experts need to possess strong programming and mathematical skills as well as knowledge of Big Data technologies and frameworks.
Demand for AI specialists
As stated above, there is a growing demand for AI specialists – especially now that companies from almost all sectors are recognising how AI can impact their bottom lines.
According to data from our freelancermap survey this year, 78% of freelancers believe that AI will see a staggering growth in the future. Last year, AI was second place with IT security being at number one.
The demand for AI experts is higher than the supply, and so professionals in this field have a great many opportunities awaiting them.
What are the skills needed for AI?
Working as an AI expert requires you to have a great number of skills that are both technical and soft. Of course different roles will require different skills but generally the following skills are what’s most common amongst AI specialists:
Hard skills
- Programming languages: Proficiency in programming languages like Python, Java, C++, or R is crucial for developing AI algorithms and models.
- Machine Learning: Understanding machine learning concepts, including supervised, unsupervised, and reinforcement learning, is fundamental. Familiarity with popular libraries like TensorFlow, PyTorch, and scikit-learn is also important.
- Mathematics and Statistics: A strong foundation in mathematics, particularly linear algebra, calculus, probability, and statistics, is essential for understanding AI algorithms and their underlying principles.
- Data Handling: Data is the fuel for AI. Skills in data preprocessing, cleaning, and manipulation are vital. Knowledge of databases, data structures, and data visualisation is beneficial.
- Deep Learning: Proficiency in neural networks and deep learning architectures is important for tasks like image and speech recognition. Understanding concepts like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) is key.
- Natural Language Processing (NLP): For tasks involving language processing, such as chatbots or sentiment analysis, skills in NLP techniques and libraries are necessary.
- Cloud Computing: Proficiency in cloud platforms like AWS, Google Cloud, or Azure can be useful for deploying and scaling AI applications.
- Ethics and Bias Awareness: Understanding the ethical implications of AI and being aware of potential biases in datasets and algorithms is becoming increasingly important.
- Computer Vision: If working with visual data, knowledge of computer vision techniques, object detection, image segmentation, and feature extraction is crucial.
- Reinforcement Learning: Understanding the fundamentals of reinforcement learning, which involves training agents to make decisions in an environment, is valuable for robotics, game AI, and autonomous systems.
- Version Control: Familiarity with version control systems like Git is crucial for collaboration and managing code changes.
Soft skills
- Problem-Solving: AI often involves complex problem-solving and critical thinking. Being able to analyse problems, break them down, and devise AI-based solutions is essential.
- Domain Knowledge: Depending on the application area (healthcare, finance, manufacturing, etc.), having domain-specific knowledge can be beneficial for developing effective AI solutions.
- Communication Skills: The ability to communicate technical concepts to non-technical stakeholders is vital for collaborating on AI projects.
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Professional profiles within AI
There are lots of different AI experts that you can find within the AI industry. Depending on the responsibilities and skills that each one has, their job title will differ.
Let’s take a look at some of the most in-demand AI profiles:
#1 Machine Learning Engineer
A Machine learning engineer is responsible for the development of ML systems that take over difficult tasks and replace slow human effort.
They develop and deploy machine learning models and algorithms for various applications, such as recommendation systems, fraud detection, and natural language processing.
They are also responsible for data handling, result application, and the processing and optimization of AI systems.
#2 Data Scientist
A citizen data scientist lies somewhere in between a business user using self-service analytics and a data scientist who is well versed in advanced analytics.
They are tasked with analysing and interpreting complex data sets to extract meaningful insights and inform decision-making. They are also responsible for developing predictive models, documenting their findings, and communicating it with business and IT leaders.
#3 AI Research Scientist
An AI research scientist is an Artificial Intelligence expert in charge of conducting advanced research in AI and exploring new algorithms, methods, and techniques to push the boundaries of AI capabilities.
They study and evolve data science prototypes and work on cutting-edge AI problems.
AI research scientists use the latest ML tools to run experiments and generally develop techniques and processes to advance the science and technology of intelligent machines.
#4 Computer Vision Engineer
A computer vision engineer specialises in creating algorithms that allow computers to understand visual information from the world.
They explore and analyse unstructured data and gather and optimise analytics from computer vision algorithms to improve their performance.
Up until a few years ago, computer vision only worked in limited capacity. However, thanks to advances in AI and fields like deep learning, computer vision has been able to surpass even human beings in some tasks.
#5 Natural Language Processing (NLP) Engineer
A natural language processing engineer focuses on developing AI systems that can understand, interpret, and generate human language.
They work on tasks like sentiment analysis, language translation, and chatbots and work on scripts to analyse and process text data, while measuring the accuracy of language understanding.
Responsibilities as an NLP or Language Engineer usually differ based on the type of industry one works for.
#6 Deep Learning Engineer
Deep learning engineers specialise in developing and implementing deep learning algorithms and architectures for complex tasks, such as image generation, language modelling, and speech recognition.
They also carry out tasks like defining data requirements for a project as well as collecting, labelling, inspecting, and cleaning data.
AI professionals in this field develop custom neural network architectures for specific needs and convert prototyped code into production code.
#7 Robotics Engineer
A robotics engineer combines AI with robotics to design and develop intelligent robotic systems capable of autonomous movement, decision-making, and interacting with the environment.
These engineers conduct the necessary research to determine the parameters of a robotic application. They also assist with cost estimates and perform integration tests and quality control.
Robotics is considered a high-end professional career in most countries and so there are tremendous job opportunities awaiting freelancers who have professional training in this field.
#8 AI Product Manager
An AI product manager is a bridge between technical teams and business stakeholders. They are responsible for defining AI product features, requirements, and strategies based on market needs and technical capabilities.
AI product managers collaborate with and are in charge of guiding a team. They require a broader range of skills than traditional product managers in order to successfully guide AI-driven products.
#9 AI Ethics Officer
An AI ethics officer focuses on the ethical implications of AI systems and ensures that the work around data and AI in a company is unbiased and ethical. These AI specialists address issues like:
- Bias
- Fairness
- Transparency, and
- Accountability
Human beings are built to have cognitive biases – a fact that can bleed into our handling of data. And since data is the foundation for all machine learning algorithms, it’s crucial that it be fair and unbiased.
#10 AI Consultant
AI consultants provide expertise and guidance to businesses seeking to integrate AI solutions into their operations. They collaborate with other AI experts like ML engineers, data scientists, analysts and other professionals to build AI systems.
Consultants in AI offer insights on strategy, implementation, and best practices and have expert knowledge on AI and ML.
#11 AI Software Developer
AI software developers are Artificial Intelligence specialists who work on the development of AI systems. They specialise in software applications that incorporate AI components, such as chatbots, virtual assistants, and intelligent recommendation systems.
AI software developers use their programming, machine learning, and data science knowledge to get AI systems to perform tasks and essentially imitate human intelligence.
#12 AI Trainer
An AI trainer is responsible for making sure that AI responds to user inputs and requests intelligently. They use raw data to create organised and unbiased data sets.
An AI trainer teaches and educates individuals, teams, or organisations about AI concepts, algorithms, and applications through training programs, workshops, or courses.
AI training generally requires three components:
- A properly-designed and functional AI model
- Large amounts of accurate data and;
- A computing platform that is powerful enough to handle said data
AI training is a relatively new job role and is one that is set to grow over the next few years.
#13 AI Analyst
AI analysts procure, prepare, and model data utilising machine learning models and new analytical methods. They also analyse AI trends, technologies, and advancements to provide insights into potential business applications and opportunities.
Like other AI professional roles, AI analysts can be found in a variety of industries, including finance, healthcare, retail, and technology.
#14 AI Engineer
AI engineers are problem-solvers who can navigate between traditional software development and machine learning implementations.
An AI Engineer can have many responsibilities in IT organisations ranging from coordinating between data scientists and business analysts to automating processes by utilising machine learning.
#15 AI Architect
Projects revolving around AI are often hindered due to poor planning and the inability to scale. This is where AI architects come into play. AI architects are responsible for optimising AI architectures in organisations and scaling AI projects.
These professionals develop AI models that align with business goals and assist a business’s digital transformation initiatives with the help of data scientists and AI experts.
They are also in charge of auditing AI tools and practices across data, models and software engineering and deciding the open-source and commercial tools that need to be used to build the AI.
How to become an AI expert?
Working as an Artificial Intelligence expert requires you to have at least a Bachelor’s degree. This needs to be in a relevant field such as AI, Computer Science, IT, Mathematics, Data Science, Statistics, etc.
Organisations may also seek professionals who have an additional Master’s degree as well as enrollment in additional AI-related courses and certification programs.
Check out options for some of these courses below:
- Generative AI, from GANs to CLIP, with Python and Pytorch
- Financial Engineering and Artificial Intelligence in Python
- AI Product Management
- IBM Applied AI Professional Certificate
- Artificial Intelligence: Reinforcement Learning in Python
- Designing and Building AI Products and Services
How much does an AI expert make?
With the ever-increasing demand for AI specialists, professionals in this field can expect an attractive salary.
The exact amount may differ according to the position, skills and company in question of course but on average, an AI expert can make around $127,000 per year.
According to data from Nexford University, the highest paying jobs in AI are Computer vision engineering, AI engineering and Deep Learning engineering.
| Computer vision engineers | $168,800 |
| Ai engineers | $160,700 |
| Deep learning engineers | $141,400 |
| AI product manager | $128,000 |
| AI research scientist | $115,000 |
| Machine learning engineers | $109,100 |
Freelance rates for AI Specialist
AI Specialists on freelancermap charge on average:
Rates in the AI industry range between $40 and $96/hour for most freelancers.
The daily rate for AI specialists (8 working hours) would be around:
