Q: What do you do at Jumio?
A: At Jumio, I am a member of the DevOps team for Product Engineering and Machine Learning. I am responsible for creating and managing the infrastructure for our machine learning environment and making sure it is secure and easily accessible to the MLEs. It also involves development and enhancement of our in-house tools which we have built for the automation of various tasks to make the work of MLEs run smoothly 😉
Q: What was Jumio’s selling point for you – What made you say “I wanna work there!”?
A: What’s unique about Jumio is the freedom and responsibility culture. As a team we decide what should be our goals. Plus I get to work with the latest tech stack which is always challenging but exciting at the same time. I cannot deny the fact that I found some great minds here who are experts in their own domains.
Q: Before Jumio, did you have similar experiences at previous companies or was this rather new to you when you started?
A: I have never worked in an organisation with such an interesting and futuristic product.
Coming from a DevOps background and working on a product with such a high user base is the cherry on the top.
Q: What do most people get wrong about the role?
A: Most people fail to realize that the existence of a DevOps role is based on this real time practical scenario: the development team makes the (often wrong) assumption that the code being produced is acceptable to all IT stakeholders. When operations validates the code, it simply tests the scenarios that the development team has laid out. The two groups feed off each other, which causes a vicious circle to develop inside a vacuum where the rest of the organization is excluded.
Consequently, the security, networking, and other critical teams have no idea what’s going on until the product is released. At which point they start receiving emergency calls from the network operations center (NOC) and service desk to troubleshoot a product they had no hand in developing.
The solution is obvious, albeit not easy: Get development and operations teams—the very literal definition of DevOps—to work together more efficiently.
Q: How do you know when things are going really well?
A: When everyone is happy and you have rapid and frequent release cycles and you don’t have to worry about staying late in the office 🙂
Q: What challenges did you face and what did you learn from that?
A: Working in the AI/ ML environment was new for me and thus working on its infrastructure was not the same as that of a regular microservice environment. I got to learn a lot in that field.