Another week in the life of a Machine Learning Engineer! My name is Bob, and in this blog I will take you with me on a journey throughout my working week here at Enjins. I started as ML Engineer roughly 3 months ago and in this brief time I have acquired much practical know-how on  both data engineering and data science. I have done two projects for clients, worked on in-house product development and have also done many smaller activities besides. But enough for the introduction, let’s get learning!


Just as any other week, on Monday morning we start with a stand-up. During this stand-up everyone provides a quick recap of the previous week, but mostly focuses on the upcoming activities for the current week. For example: Nick shared some insights on the sales process for the week, Keje told us about a project where he needed some help with, and Tevhide told us about the model she is going to train for a client. And me? I have a pretty exciting week ahead: setting up a machine learning infrastructure for a FinTech client and working on the front-end of our in-house product.

Keje and I are both working on the project for the FinTech. We spent the rest of the Monday deciding on which AWS instances we need to spawn for the specific needs of the client. We also started with this setup and prepared for a meeting tomorrow.


Today Keje and I are working at the fintech’s office. The day started with an elaborate meeting with the representative, in order to provide all the thoughts and designs we have come up with for the infrastructure so far. This was a typical situation where business meets IT, as most technical terms are not self-explanatory to the representative. It is very motivating though when our simplified explanation of the tech was understood and received well. Kudos to us!

After the meeting, we started to load up a docker image on an instance in AWS. This docker image contains an airflow configuration, which we are going to use for the ETL processes. We also started setting up the ETL processes, in order to make sure that the data of the client is both accessible and workable.


This Wednesday is fully dedicated to a product that we are developing at Enjins. This product is about ML Ops, focusing on easing the process of deploying and monitoring of machine learning models. This product entails a set of microservices and a frontend that enable deployment of models within 10 minutes. Since I have quite some experience in the field of front-end development, I got onboard with the development of this product. The main goal for today was to implement a dashboard on the homepage, so a user can quickly see and monitor how the currently deployed models are performing. This dashboard includes some graphs and a summary of the status of the currently deployed models.

And of course, as with any software development, debugging also needs to be done. In the afternoon I got out the electric mosquito racket and starting zapping some bugs! Just kidding of course, but disclaimer: bugs were harmed.


Another day in the office. Today’s focus: preparing the learn-at-lunch for Friday and transferring the yearly dataset of NBD-Biblion into the analysis database. The learn-at-lunch is an Enjins ritual: every Friday during lunch 1-2 people explain / teach on a certain topic, mostly directly relevant to our daily work activities. As Keje and I explored the ECS (Elastic Container Service) of Amazon deeply, we wanted to share this knowledge. 


In the afternoon I spent most time on a Biblion case, where the yearly results of the libraries of the Netherlands needed to be mapped and uploaded into the analysis database. This work consisted mostly of mapping the features of the input dataset against the target data table.


This day didn’t end in the afternoon though. Maarten, Lars, Keje and me planned a coding session this evening. As part of a service contract with one of our clients, we came up with the idea to create a ’Service Alarm’ using a raspberry pi. We ordered some food and started coding a small script that checks an email inbox for new service emails that requires are attention. We also hooked up a speaker to the raspberry pi. And now, as soon as we receive one of these emails, an alarm sounds around the office. Yay!



Ah, the good old Fridays. On Friday everyone works at the Enjins office, making it a very ‘gezellige’ day. In the morning I discussed my progress regarding the Biblion case with Tim, who is leading the project. We were even able to finish the mapping and upload to the analysis database! Right after that I started preparing and setting-up the learn-at-lunch.

I spent the rest of the afternoon on the in-house product again. This time I worked on the authentication system of the application. In order to start using our own product, I created some credentials for everyone. It turned out not to be so easy, but a good hustle with the code and 2 hours later ended in a successful end of the week.

Workweek of a ML Engineer

Being a Machine Learning Engineer at Enjins means that you have a diverse job. Besides training your algorithms, the job includes many other aspects such as preparing a project, understanding the business, and implementing engineering elements. For me, the combination between the tech (data science/engineering) and the business makes my work attractive. Combine this with awesome colleagues and you have great place to work at! If you have any questions regarding the job, please feel free to contact me on my LinkedIn or by using the contact button below.

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