• Chris Armbruster

Machine Learning Engineer (m/f/d). Competency Profile No 3.

Updated: May 31

The competency profile validates your domain expertise in data. It recognizes you as a Senior practitioner or advances your career to the Senior level.

By practitioners, for practitioners - this service is provided by AI Guild members recognized for their expertise.

Here is the example of a Machine Learning engineer.

She has 2+ years of automotive industry experience with a prior track record in computer science and algorithm development. Her focus is on process automation by Classical Machine Learning.

What do you see in the competency profile?

This profile shows an excellent focus on building Machine Learning models

  • Strong domain expertise in building the proof-of-concept; flanked by

  • Skill in data preparation for the PoCs, bolstered by years of prior experience as a researcher.

As companies build expertise, the Machine Learning models go to production, and so the profile shows

  • Consolidated competence for deployment; and a

  • Skill extension to Deep Learning.

I think the profile exemplifies the journey of a practitioner in the European industry over the last years. Iteration on PoCs was quite common, as companies were looking to adapt to the new technologies. On her own accord, she got ready for the next step by seeking MLOps challenges. To me, it is a brilliant move to build deep expertise in deployment. She certainly has the background to excel.

The competency profile was built as part of the AI Guild career development program.

Focus: What is in and what has been left out?

Perhaps you noticed that there is no mention of data analytics and data visualization. It is a choice, and the competency profilöe facilitates you in selecting the areas in which you have deep domain expertise and are looking to deepen your expertise. That focus moves your career forward in the desired direction.

Where is her #datacareer headed?

She is building a portfolio that gets her ready for technical leadership in Machine Learning. Within a couple of years, I expect her to be designing and supervising end-to-end ML deployment. She already has the competence for model building with data preparation. Commensurate experience in deployment means that any company would have a go-to person for the successful productionization of Machine Learning. It is an excellent value proposition.

How to get your competency profile?

Are you in Year 2, 3, or 4 of your #datacareer? Would you like to find out how you efficiently show and develop your expertise? It takes 60 days to complete the competency profile. All you need to do to start is point us to your LinkedIn or GitHub profile (or similar) here.

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