Are you interested in a career in AI ML, but the multiple job profiles seem too confusing? AI and machine learning engineer jobs need skills and knowledge aligning with disciplines such as information theory, programming, and computer science.
The global artificial intelligence market size is projected to reach $1,811.8 billion by 2030. Now is the time to enter the AI job market and carve a niche for yourself with honed skills and expertise.
In this article, you will learn about 12 machine learning and AI jobs that you can consider for your technical career.
Here’s what we’ll cover:
- What is Machine Learning?
- What is Artificial Intelligence?
- ML and AI Jobs: Requirements
- 12 Machine Learning and Artificial Intelligence Jobs
- Why Should You Work in MI and AI?
- Key Tips To Land ML and AI Jobs
- Crack ML and AI job Interviews with IK
- FAQs about AI Jobs
What is Machine learning?
Machine learning is a part of artificial intelligence and computer science that focuses on the use of algorithms and data to imitate the human learning process to improve its accuracy. Machine learning is a crucial component of the data science field. By using statistical methods, algorithms are trained for making predictions or classifications and for uncovering the key insights in data mining projects. With the help of these insights, decision-making of businesses and applications takes place, which impacts growth metrics. With the increasing data, the demand for AI and machine learning engineer jobs is rapidly increasing.
What is Artificial Intelligence?
The imitation of the human intelligence process through machines and computer systems is called artificial intelligence. Certain AI applications include speech recognition, export systems, machine vision, and natural language processing. By investing huge amounts of training data, analyzing the data for patterns and correlation, and utilizing these patterns for making future predictions, the working of AI systems takes place.
ML and AI Jobs: Requirements
If you are willing to pursue a career in machine learning or artificial intelligence, you must hold
- a stable background in mathematics, information theory, and computer science.
- Professional certificates or degrees in data, science, information, technology, software, engineering, or computer programming.
Some employers even seek candidates with industry certificates with area expertise. Apart from technical knowledge and professional credentials, you must also enhance your skills in areas such as leadership, critical thinking, communication, and teamwork.
12 Machine Learning and Artificial Intelligence Jobs
You can explore and craft your career in artificial intelligence and machine learning in multiple positions. Some of the ML and AI jobs are:
Computer Programmer
A computer programmer is responsible for applying and testing codes that are required for software and computer functionality.
Key Responsibilities:
- They can even specialize in a certain programming area, including software development and machine learning.
- While crafting applications and computer programs, they can collaborate with engineers and developers.
- Moreover, computer programmers were responsible for translating software designs created by engineers into codes that are used by network systems and computers to operate.
Average Salary: The average salary of a computer programmer is $57,087 per year.
Information Systems Technician
As an information systems technician, your duties include programming, installing, and configuring electronics, network and software applications, and hardware.
Key Responsibilities:
- Information systems technicians function with information, technology, teams, networks, architects, and systems designers to integrate technical solutions that resolve functionality problems.
- Â In artificial intelligence, technicians help developers and engineers with the maintenance and integration of machine learning systems.
Average Salary: An information systems technician earns around $59,266 per year
Systems integration specialist
As a systems integration specialist, there are several responsibilities and duties to be performed.
Key Responsibilities:
- A systems integration specialist’s responsibilities are to develop, install, and maintain integral components in data management and network systems that include soft, artificial intelligence, and machine learning.
- They are even responsible for performing services such as security, protocols, internal and external data transfer, and file management.
- Specialists also help development teams to integrate machine learning systems and artificial intelligence data processes.
Average Salary: The average salary of a Systems integration specialist is $77,969 per year
Robotics engineer
With the combination of computer-aided design, practices, and machine learning, a robotics engineer develops designs for multiple robotic applications.
Key Responsibilities:
- They assist mechanical and manufacturing engineers, CAD technicians, and designers in processing design plans and producing robotic machinery for multiple uses.
- Robotics engineers even assist in developing innovative artificial intelligence and automated solutions for supporting industrial and manufacturing needs.
Average Salary: A robotics engineer earns approximately $90,075 per year
User Experience Designer
A user experience designer relies on data about the actions and behavior of individuals using products, applications, and services.
Key Responsibilities:
- In artificial intelligence, UX designers help programmers, engineers, and developers to process the integration, modification, and tracking of the performance of soft and automated AI applications.
- Evaluating user experience is crucial while creating automated and interactive applications and US designers or professionals who oversee updates and improvements for engaging large audiences.
Average Salary: A User experience designer makes around $91,015 per year
Systems Engineer
There are multiple duties that you need to handle as a systems engineer.
Key Responsibilities:
- A systems engineer’s responsibilities include developing, creating, testing, and analyzing automated systems, network systems, and multiple organizational systems for improving functionality and efficiency.
- In artificial intelligence systems, engineers develop and maintain artificial networks and automated systems.
- They even maintain industrial and manufacturing systems to automate processes to ensure the efficient working of operational programs.
Average Salary: As a Systems engineer, you can earn $95,466 per year
Full Stack Developer
Full-stack developers manage both back-end and front-end processes in the development.
Key Responsibilities:
- Maintenance and integration of machine learning, automated, and soft AI processes.
- They even help with several phases of system development that include interface, design, user experience, network, architecture, hardware, functionality, and systems design.
- Full-stack developers assist software engineers, programmers, and technical professionals in creating systems to support business processes.
Average Salary: A full-stack developer earns $106,043 per year.
Data scientist
A data scientist specializes in machine learning and artificial intelligence.
Key Responsibilities:
- They apply predictive analysis and reports for organizing, evaluating, and modifying data within machine learning and automated systems.
- They even study data to understand the process of how AI systems respond to imports that are required for performance and functionality.
Average Salary: The average salary of a data scientist is $117,806 per year
Deep Learning Engineer
A deep learning engineer utilizes machine learning and artificial intelligence applications for designing and creating artificial neural networks.
Key Responsibilities:
- They assist data scientists and computer programmers in developing and integrating natural languages used by deep learning systems to function.
- Moreover, they even analyze and modify the frameworks created to improve the accuracy and functionality of execution.
Average Salary: A deep learning engineer earns $126,297 per year.
Network Architect
There are numerous responsibilities that you need to perform as a Network Architect.
Key Responsibilities:
- A network architect designs and crafts communications and data networks, including internet and wide and local area networks.
- In artificial intelligence, architects act as senior data scientists and plan, evaluate, and develop data integration for improving artificial networks, machine learning, architecture, and automated systems.
Average Salary: The average salary of a network architect is $127,121 per year
Software Architect
Software architects function in the development, design, and application of programming solutions and computer software.
Key Responsibilities:
- The work in collaboration with development professionals for gathering and interpreting artificial intelligence and machine learning analytics to use in improvements and modifications to present AI processes and automated processes.
- Software architects even work on the Predictive analysis and design processes of machine learning algorithms to promote effective technical solutions.
Average Salary: As a software architect, you can earn an average salary of $134,778 per year.
Machine Learning Engineer
Machine learning engineer is responsible for working with AI, programmers, data scientists, and AI architecture for designing and implementing artificial intelligence applications.
Key Responsibilities:
- They also look after the testing programming and running modification of automated systems and assist technology teams in improving AI control systems and robotics.
- With the application of computer programming, data, science, and mathematics machine learning, engineers craft infrastructures and integral components on which several industry practices rely to function.
Average Salary: As a machine learning engineer, you can earn an average salary of $140,277 per year
With the rising machine learning and artificial intelligence in each sector, the demand for skilled machine learning and artificial intelligence professionals is rapidly increasing. Hence, according to your knowledge, skills, certifications, and degrees, you can diversify your career in any of the ML and AI jobs stated above.
Why Should You Work in ML and AI?
People who have a background in statistics, computer science, engineering, technology, or math can gain multiple benefits by working in ML and AI jobs as compared to other industries. Some of the benefits of working in machine learning and artificial intelligence industries include:
Impact
In several crucial industries such as finance and healthcare, machine learning and artificial intelligence play a major role, which makes it beneficial for job seekers to pursue a career in this field as there are multiple opportunities.
Stability
Machine learning and artificial intelligence are not going to disappear anytime soon. Due to this reason, it offers high stability and is the best way to secure your future and career by being a part of this field.
High Demand
Machine learning and artificial intelligence have been innovative. Recently, there have been a number of job openings and fewer people who can compete for these ML and AI jobs. This provides benefits for job seekers with ample choice between roles and companies, along with opportunities, salaries, and work arrangements for enhancing their careers.
Key Tips To Land ML and AI Jobs
Before you explore and apply to any machine learning or AI jobs, you must consider the following points:
Look Beyond Major Tech
Some of the biggest tech companies are the biggest recruiters of ML and AI professionals. However, you can also seek these roles in other sectors, such as banking, pharmaceuticals, retail energy, and agriculture.
Master Programming Languages
Even though there are a few soft skills that are beneficial for occupying a machine learning or artificial intelligence position, your technical skills matter the most to the recruiters. PyTorch, Python, and TensorFlow are some of the programming languages that you must master to grab any AI job.
Be Adaptable
As the technicality and frameworks applied in artificial intelligence and machine learning are rapidly changing, act as a candidate who can adapt and learn new things efficiently. Act as a good communicator and express yourself clearly, be a problem solver and work in collaboration with different teams to overcome obstacles and improve your working efficiency.
AI Communities
Look for online groups and forums that especially focus on machine learning and AI, and master your skills by joining these communities.
Sample Work
Just mentioning your skills and certifications on your resume is not enough. Create portfolios with your sample work, which the recruiters can analyze to acknowledge your capability, knowledge, and skills.
Crack ML and AI Job Interviews with IK
You can find multiple positions in various sectors to work as a machine learning professional or jobs in artificial intelligence. As the innovations in ML and AI are constantly rising, so are the opportunities and job positions.
In the field of AI careers, whether you’re willing to be a data scientist, machine learning engineer, or software architect, with the right skills, great knowledge, years of experience, certifications, and suitable degrees, you can outstand the competing candidates and grab the job you’ve always been dreaming of. Grab the best AI and ML job opportunities with Interview Kickstart’s ML and AI Courses!
FAQs about ML and AI Jobs
Q1. Do AI and ML work together?
As ML is an application of AI, they do work together.
Q2. Are AI and ML good careers?
Yes, AI and ML are good career choices because they offer multiple professional paths, rapidly growing opportunities, and numerous job openings with good pay scales.
Q3. Which is the best field for ML?
Software programmers, software developers, data scientists, and computer engineers are some of the best fields of ML.
Q4. Is ML used in all AI?
No, not all the AI solutions are ML. However, all ML solutions are AI.
Q5. Is ML easier than AI?
Even though both fields are highly complex, learning ML is considered easier than AI.
