There’s a huge surge in investment into AI over the last number of years in the UK. One third of AI start-ups in Europe are based in the UK and there’s been a 600% increase in the number of UK AI companies in the last ten years, according to gov.uk. It’s not surprising then that demand for technical specialists with expertise in artificial intelligence is on the rise, with employers willing to pay higher salaries for those with AI experience and qualifications such as a MSc in Artificial Intelligence. If you’re currently making career choices and are interested in the field of AI, then read on the find out about these five AI careers with an MSc in Artificial Intelligence:
- Data Scientist
- Machine Learning Engineer
- Robotics Engineer
- AI Research Scientist
- Deep Learning Engineers
What is artificial intelligence?
Artificial intelligence is the science of developing technology to imitate human intelligence. AI is all around us today: in our homes, at work, and used in almost every sector of industry including retail, healthcare, and business. Examples include recommendations on your Netflix account, facial recognition when you use your phone, the use of robotics in surgery, chatbots on websites, amongst others.
This technology is growing in scope and being improved all the time. Therefore, AI specialists are in demand for their skills, opening many AI careers.
AI careers
Data Scientist
A data scientist retrieves huge amounts of data from numerous sources, analyses the data and interprets them to make recommendations to solve a problem or make an improvement. They use their knowledge and skills in machine learning, data mining, statistics and AI to help them do this. They work in various sectors such as healthcare, law, government, finance, retail, academia and IT.
Day to day tasks of a Data Scientist can include:
- Retrieve and merge data
- Analyse data for patterns
- Develop and test new algorithms
- Develop predictive models
- Simplify data problems
- Write up reports of findings, including data visuals and share with others
Examples of how Data Science is applied:
- In logistics, data science helps to improve and inform shipping routes
- In healthcare, data science can help with predicting disease and personal healthcare recommendations
- In environmental sector, data science can help with climate modelling
- In sports, it can track and gather performance of athletes
Read more about becoming a Data Scientist.
Machine Learning Engineer
Machine learning is a branch of artificial intelligence, and a machine learning engineer’s job is to create algorithms and computer programs that enable computers to learn and act without further programming.
Machine learning engineers will have strong maths skills and will be able to work with non-computing colleagues to define requirements and explain complex solutions. This is a relatively new and expanding field where employers require a high level of expertise so gaining an MSc in a subject like Embedded Computing and Machine Learning can be advantageous. Computer programming is an essential requisite with knowledge of Java, Python and C++.
Day to day tasks of a Machine Engineer can include:
- Check existing models are running as expected
- Write code, design databases for new models/projects
- Build on and improve performance of existing models/projects
- Test models
- Respond to requests including ‘out-of-hours’
- Attend meetings, pitch product ideas
Examples of how Machine Learning is applied:
- Facial recognition, e.g. on your mobile and social media apps
- Marketing, e.g. product recommendations
- Social media, e.g. offer content in line with user’s preferences
- Email spam filtering
Read more about becoming a Machine Learning Engineer.
Robotics Engineer
Robotics engineers use their knowledge and skills of computer programming, engineering design, and artificial intelligence to design, enhance and build machines that can perform automated tasks. Robotics exist and serve in many industries including manufacturing, healthcare, space exploration, and warehousing.
Day to day tasks of a Robotics Engineer can include:
- Design, build and test robots
- Create the software systems to control the robotic systems
- Teach plan paths to robots
- Evaluate prototypes
- Provide technical support and fix faults
- Research new ways to use robots and new breakthroughs in robotic systems
Examples of how Robotics is applied:
- In healthcare, e.g. surgical robots
- In supply chain, e.g. in warehouses, packing and palletizing
- On assembly lines in manufacturing
- Agriculture and farming
There are various routes to becoming a robotics engineer including:
- Apprenticeships such as robotics engineer degree apprenticeship
- University such as undergraduate or postgraduate degree in robotics engineering, computer science or AI.
- Working your way up from robotics technician with further training and qualifications.
AI Research Scientist
AI research scientists research and develop new artificial intelligence technologies. They very often work in academia but can also work for tech companies as part of the research team specialising in development. They are highly skilled individuals with a minimum of a master’s degree in a computer science, AI or machine learning but will often have or be working towards a PhD.
They have excellent programming skills, a firm understanding of maths and statistics, and ability to handle big data. AI research scientists also possess soft skills in problem solving, strong analytical skills, research, communication and collaborative approach.
Day to day tasks of an AI Research Scientist can include:
- Read research papers; generally, research what’s new in the latest thinking/findings on AI
- Collaborate and talk to AI peers and industry leaders
- Write code / design algorithms, test and evaluate
- Work towards getting own research published
- Teach if in academia
- Reflect on work, work of others, and AI in wider context
NLP Engineer
NLP stands for natural language processing and is a subset of artificial intelligence that looks at developing machines to understand and generate human language. An example of natural language processing are chatbots. NLP engineers are therefore the engineers that develop the technology to enable this.
NLP engineers may have undergraduate degree in subjects like computer science, data science or linguistics. As well as programming skills and understanding of algorithms, they need to understand the development of AI and machine learning; a master’s degree in a subject in AI or machine learning could be helpful in that respect.
Day to day tasks of an NLP Engineer can include:
- Clean data; define datasets
- Read NLP papers / stay up to date on latest research on NLP and AI
- Design, build and continuously optimise NLP systems and models
- Collaborate with Data Scientists and Software Developers
- Report feedback and insights to Product Managers
Examples of how NLP is applied:
- Customer service, e.g., chatbots, smart assistants
- Translation apps
- Predictive texts, autocomplete, auto correct, spell checker, grammar checker
- Search engines
- Speech recognition
How can I get ahead in my AI career?
A study conducted by ARU and published in the journal Oxford Economic Papers, revealed that employers were more likely to offer job interviews and higher wages to those who have studied AI.
They found that 54% of male applicants with AI capital (took at least one AI module as part of their studies) were offered a job interview compared to 28% who did not have AI capital. For female applicants 50% of those with AI capital were offered job interviews compared to 32% who didn’t. In terms of job offering wages, males with an AI qualification were offered on average 12% higher wages than their male counterparts without an AI qualification; and females with an AI qualification offered on average 13% higher wages.
Transferable skills you need for a career in AI?
To work in the field of AI, it’s helpful to develop these transferable skills:
- Think creatively and be adept at problem solving
- An analytical approach
- Excellent communication skills to explain technical terms to non-tech people
- Work collaboratively within a mixed team of technical experts and non-technical colleagues
- Research skills with an experimental mindset to push boundaries of discovery in this developing field
You’ll get a chance to exercise these skills, particularly creativity, problem solving, analytical and research skills, throughout most master’s degree in AI related subjects, where you’ll engage in real-world projects or interdisciplinary learning like hackathons.
Challenges in AI
The technology of AI is moving fast, and we are seeing its reach spreading rapidly into our daily lives. With this comes challenges, such as keeping up with rapid technological changes and ethical concerns. Investing in a postgraduate degree in AI can help you assess these challenges.
Look out for modules that foster an ethos of continued professional and personal development, place an emphasis on keeping abreast of latest AI research and technology, encourage critical thinking and examines the ethical implications of AI.
Can I still move into an AI career if I’m a non-technical professional?
Inspired to move into an AI career?
If you feel inspired to start your career in the exciting and fast-emerging area of artificial intelligence or you’re currently working in a tech related role and want to change your career to a more AI focused job role, then consider upskilling or reskilling with a related master’s degree.
Next steps can include:
- Attending AI webinars
- Looking at foundation degrees if this applies to you
- Researching various MSc programmes
- Researching deeper into various AI careers
All our online advanced computing and AI degrees are designed for those with busy lifestyles and want to study flexibly through 100% online distance learning. It means you can continue to work and juggle your family commitments without compromising on your future career aspirations.