A Snapshot of AI and ML Engineering Market Trends

Unsurprisingly, explosions in artificial intelligence (AI) and machine learning (ML) development are driving much of the growth being seen in the data science field, as well as shaping the jobs in high demand, including brand new roles in the AI and ML engineering space that are centred around ChatGPT and natural language processing (NLP).

Rosie O’Callaghan, Business Manager at Harnham, works alongside a wide range of candidates and employers in the data science space and gives her insights as to the trends she has been witnessing in recent months.

What roles are increasing in popularity?

We’re seeing an increased interest in data scientists who are full stack, which means they’re able to manage a process from ideation through to deployment. Demand for ML engineering roles has also been rising over the last few years, with a recent shift towards MLOps (machine learning operations) professionals who can bridge the gap between data scientists and data engineers. Tasks might include setting up the ML platforms to facilitate data scientists to build their models.

Employees with this cross-over skillset are an invaluable asset to businesses. Data scientists are not necessarily engineering experts and vice versa, so it’s beneficial to have team members who understand and can support both elements.

With volumes of data increasing every day, data scientists and data engineers have a lot on their plates, so companies are always looking to streamline and improve processes wherever possible. To do this, some are choosing to differentiate tasks down to a granular level, making specific employees responsible for very specific roles – hence the rise of MLOps.

However, demand for MLOps professionals is not consistent across the board – there are businesses who still expect data scientists or data engineers to take on everything end-to-end and are not interested in hiring more specialised talent.

Moreover, the field is still brand new, so whilst some companies are beginning to build out brand new MLOps teams, others don’t yet have this function. It is also somewhat unclear as to where these roles should sit within a business, so some are still determining where best to place these types of candidates.

What skills are hiring managers looking for at the moment?

Hiring managers are increasingly seeking candidates with strong technical abilities paired with high commercial acumen, storytelling capabilities and stakeholder management skills, enabling them to demonstrate and ‘sell’ the business value of their work. In other words, employees that can not only complete technical processes, but also explain the results from a business point of view, are in high demand across the market.

Employers are also keen to find talent with a combination of data science knowledge and engineering expertise, as well as those who have experience with the cloud, which has quickly gone from being a ‘nice-to-have’ to a ‘must-have’ across sectors. For MLOps roles, those familiar with software engineering best practices, such as systems design, are highly sought after.

What are candidates looking for?

Candidates are eager to hone their engineering skills and are looking for roles that will expose them to project deployment. There is also interest in developing experience with new emerging technology, such as generative AI and taking on projects that employ Chat GPT.

Many are interested in developing more specialist expertise, and there has been a marked increase in niche university courses and training programmes being developed to match new areas of AI. For example, Edinburgh University is now running courses in AI and Deep Learning. This is started to be reflected in the candidate market.

When it comes to salaries, our recent Data & AI Salary Guide found that there is a disconnect between pay expectations and reality. When changing roles, employees are still demanding high rates despite actual rises in pay returning to less inflated levels – for example, some are expecting a 22 per cent pay increase and only receiving an average of 16 per cent. This is reflective of last year’s trend of candidate remuneration running far above the market rate, heightened by tech giants able to offer sky-high salaries and establishing benchmarks which are unattainable for most businesses.

What is impacting hiring?

Whilst most companies are looking to hire into and expand their AI and data science teams in the longer term, in the short term many are still risk adverse, and are hiring to a lesser extent than predicted. If companies have any doubt as to whether they need to hire someone new, they likely won’t, and will instead look internally at who could take on the new responsibilities. This is largely due to rising costs and stretched budgets, but also because no one wants a repeat of the mass tech lay-offs of the previous year, so are taking a more conservative approach with their hiring strategies.

That said, there are signs that hiring into AI and ML positions is picking up, with increasing numbers of roles being advertised. 47 per cent of respondents in our UK salary guide believe that AI and ML will have the biggest impact on the data market this year, and as more companies realise how emerging technology could benefit them, we’re likely to see many begin to invest time and money into building out these teams.

If you’re interested in joining this burgeoning sector, or are looking to expand your data team, get in touch with our team of experts today, who would be more than happy to help.