Machine Learning Operations (MLOps) is a discipline that is rapidly growing within the Artificial Intelligence industry. The need for MLOps Engineers has been climbing and is predicted to be one of the most in-demand hires over the next fives years. Unfortunately, it can be extremely difficult to find individuals who are successful in these roles due to a known shortage of experienced professionals. If you’re struggling to find top MLOps talent, here our top tips:Don’t get Caught up on Job Titles Hiring Managers are frequently finding challenges identifying if someone is capable of performing as a high-level MLOps Engineer and this is typical if you base your search on candidates job titles. Since the MLOps field is younger than other, more common, fields in AI there are drastically fewer professionals with the title MLOps Engineer, with this title only emerging in the past couple of years. The key thing to look for are engineers who have proper tooling. For example, a Software or Data Engineer would usually have the needed tooling to easily transition into an MLOps Engineer role, but conceptually do need to understand the nuances of Data Science and Machine Learning. Hiring from the Bottom UpWhen a company starts to build out their MLOps function, it is currently very unlikely that they will be hiring seasoned executives given that the concepts of MLOps are much more recent. This person will much more likely be a hands-on specialist and the founding member of the function. This does not have to be seen as an obstacle, building out an MLOps function will require an individual who wants to work closely with data and, if you already have an executive who understands the value of building out this area, then there is always the option to hire a freelance MLOps Engineer to get things off the ground quickly. Make the Role and Responsibilities Crystal ClearOne of the biggest challenges is figuring out how and where MLOps sits within the organization. One of the obstacles candidates who want to pivot into an MLOps Engineer role face is the lack of clarity in the role and its responsibilities as a direct contribution from the lack of clarity at an organizational level, a problem most experienced with startups. The best way to combat this is to understand exactly how MLOps can benefit the organization and outline the exact needs an MLOps Engineer can satisfy by joining the team. Don’t Assume an MLOps Engineer will stay foreverBuilding out an MLOps function can be extremely difficult if the person who created the infrastructure leaves and this is a major problem employers are facing in the field, particularly with the shortage of MLOps Engineers. It’s critical to make sure everything an MLOps Engineer builds is documented for later use, person dependent and as reproducible as possible. Don’t get Lost in Resumes As important as a good resume can be, for a crucial hire in a premature field what is most important is finding a candidate who has an architectural mindset and excellent tooling. This can be easily achieved if an interview process is tailored to test these two things. A great way to do that is by giving candidates hands-on tasks that are both realistic and simplified.Be Particular When It Comes to AdvertisingIt can be tempting to shy away from putting a safe title on a job description. Many people see it as risky to advertise a role as an MLOps Engineer because it can be intimidating to professionals who want to pivot into MLOps from other titles. Do away with Data Science Engineer, Machine Learning DevOps, and Software Engineer (MLOps). Keep it simple and advertise the role as what it is – an MLOps Engineer role. The candidates you want to attract will not shy away. If you’re looking to build out your Data team or transition into an MLOPs career, Harnham can help. Take a look at our latest MLOps jobs or send me a message to find out more.
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