So from what I understood you want to see potential groups/clusters in the set of CVs. the idea of cvdict is great, but you also need to convert all texts to numbers ! you are half way through. so think about matrix/excel sheet/table. where you have the profile of each employee in each line. name1,cv_text1 name2,cv_text2 name3,cv_text3 ...
Yes, as you can guess, the length of cv_text can vary. Some people have a lengthy resume some other not ! which words can categorize the company employee. Some how we need to make them all equal size; Also, not all words are informative, you need to think which words can capture your idea; In Machine Learning they call it "Feature" vector or matrix. So my suggestion would be drive a set of words and mark if the person has mentioned that word in his skill.
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name1 1 1 0 0 0
name2 0 0 0 1 1
name3 0 0 1 1 0
or instead of a 0/1 matrix you can put how many times that word was mentioned in the resume. again you can just extract all possible words from all resumes. NLTK is an awesome module for doing text analysis and it has some built-in function for you to polish you text. have a look at the first half of this slide.
Then you can use any kind of clustering method, for example hierarchical https://code.activestate.com/recipes/578834-hierarchical-clustering-heatmap-python/ there are already packages for doing such analysis; either in scipy or scikit and I am sure for each you can find a tons of examples. The key step is the one you are already working on; representing your data as a matrix.