Machine Learning Engineer (m/f/d). A competency profile.
Updated: Aug 28
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 an example of a Machine Learning engineer.
She has an industrial engineering background and substantial prior programming experience. After completing a Data Science Master's 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 highlighted that she has years of experience and has repeatedly done critical tasks. You are looking at a Senior-level must-have profile for any company deploying.
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 no explicit mention of Data Science, Deep Learning, or Neural Networks in the competency profile. While related frameworks and tools are in her toolbox, they are left out to facilitate focus for the practitioner and you, the reader of the 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 explicitly mentioned and 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 not 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.
Do you want to progress to Senior, Lead, and Director?
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