Machine Learning Engineer (m/f/d). A competency profile.
Updated: May 27
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 an industrial engineering background and substantial prior programming experience. After completing a Data Science Master mid-career, she has been in the industry for three years, at a corporate subsidiary and a startup.
What do you see in the competency profile?
This profile shows substantial, deep expertise in two fields:
The ML engineering value chain with data modeling, feature engineering, backend services, and job orchestration; and
ML Ops for securing quality deployment.
She complements her focus on proficiency for end-to-end solutions with
A wider background in Machine Learning; and
An emerging interest in Natural Language Processing.
The profile is very technical. As you can see, we took care to highlight that she has years of experience and has done critical tasks repeatedly. You are looking at a Senior-level must-have profile for any company deploying. It was built as part of the AI Guild career development program.
Focus: What is in and what has been left out?
The competency profile is explicit on technical detail, showing proficiency with multiple toolboxes. The practitioner has not nailed her flag to any specific ML framework or cloud solutions provider. I find that intriguing as it indicates flexibility in the approach and confidence in her mastery of the ML engineering value chain.
Perhaps you noticed that there is no explicit mention of Data Science, Deep Learning, or Neural Networks in the competency profile. While related frameworks and tools principally are in her toolbox, they are left out to facilitate focus for the practitioner and you, the reader of competency profile. Wouldn't you love to talk to her about architecting end-to-end solutions, deploying products, and the expertise she holds from doing this many times?
Where is her #datacareer headed?
The shifting focus to NLP is mentioned explicitly and also substantiated with initial expertise. What you see here, I think, is a very accomplished ML engineer that is building on her existing proficiency to enter a new domain (NLP in education). However, this is no a re-tooling or change of career but extends her considerable expertise into a new domain. It is a viable move for practitioners and a very interesting one as it enables you to compare industries and their technical solutions, facilitating innovation and better practices.
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.