• Chris Armbruster

Computer Vision Engineer (m/f/d). Competency profile No 2.

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 Computer Vision engineer.

He has a Master's degree in Image Processing and Computer Vision and started two years ago in an early-stage startup. It gave him the opportunity for product development from scratch in a small team. Subsequently, the startup received venture capital funding.

Computer Vision engineer. Mid-level competency profile.


What do you see in the competency profile?

Your attention probably is drawn to the column on end-to-end Computer Vision, which defines the profile. He has run through the process twice, from scratch to deployment. The second product received significant investment. The other pillar is a professional statement, offering a perspective on how Computer Vision practice is "grounded on the union of image processing and deep learning."

You are looking at the competency profile of a mid-level practitioner for whom joining an early-stage startup has paid off.

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 some of the things that the profile is not highlighting, e.g., Deep Learning models or research, NLP, or Data Science generally. It is not uncommon for practitioners in the early years to try out different roles or technologies. Sometimes a move sideways allows you to re-focus and then move forward.

Data roles and domains are becoming more specialized. Specialization advances AI adoption. Advancement facilitates creating footholds on career tracks that increase professional satisfaction and impact. The advantage of a wholly focused competency profile: You are clear about the impact you seek.


Where is his #datacareer headed?

You may ask: Is the focus on Computer Vision only not too narrow?

Let's take a second look. Deep Learning here is relevant mainly as an enabler of Computer Vision, and image processing is the technology powering it. We had a long discussion about images not just being another type of data but a 'data type.' For example, numbers, images, and texts are different data types. Further, in the field of Computer Vision, it makes a difference if you are dealing with, e.g., medical images, the human body, or autonomous mobility devices. As we advance AI adoption, expertise in domain data will be precious.

I think that the 'narrow' focus is what makes this competency profile exceptional. It shows the drive to master a domain and deliver end-to-end. I expect a much-accelerated career.


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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