The highest standard in AI — AI Guild #datacareer
Machine Learning Engineer (m/f/d).
A competency profile in the automotive industry.
In industry and startups, ML is being productionized. What does it take to be considered for technical leadership?
She is a Machine Learning Engineer
She has 2+ years of automotive industry experience and a prior computer science and algorithm development track record. Her focus is on process automation by Classical Machine Learning.
For companies and practitioners: The competency profile validates your technical competency and domain expertise in data. It recognizes you as a specialist and advances your career to Senior, Lead, and Director. By practitioners, for practitioners — this service is provided by the AI Guild.
What do you see in the competency profile?
This profile shows an excellent focus on building Machine Learning models
- Strong domain expertise in making the proof-of-concept; flanked by
- Skill in data preparation for the PoCs, bolstered by years of 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.
The profile exemplifies a practitioner’s journey in the European industry over the last few years. Iteration on PoCs was expected, 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. It is a brilliant move to build deep expertise in deployment. She certainly has the background to excel.
How does a competency profile indicate potential?
You may have noticed no mention of data analytics and data visualization. It is a choice, and the competency profilöe facilitates selecting the areas where you have deep domain expertise and want to deepen your knowledge. That focus moves your career forward in the desired direction.
Technical leadership
She is building a portfolio that prepares her for technical leadership in Machine Learning. I expect her to design and supervise end-to-end ML deployment within a few years. She is already competent in model building and 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.
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