A day in the life of an Engineer at Viqal

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November 8, 2023
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Hi Nikolay, you are an MLOps Engineer at Viqal, what do you work on?

A: "Being an MLOps Engineer is a fancy way of saying I work on everything from the moment emotional data comes in the cloud to the moment it is shown to clients. This includes data pre-processing, training, inference and evaluation of Machine Learning models, all the way to data serving and data visualisation. The cool thing is everything happens in the cloud using AWS services and Docker containers and we try to stay as flexible as possible so we can quickly introduce new insights to our clients."

Q: "Do you deal with interesting problems?"

A: "You bet! We deal with an interesting and complex problem - inferring emotion from hundreds of voice features. Moreover, we do this in real-time which requires us to have a stable data pipeline from the physical sensor on-site all the way to the data dashboard or API that clients get insights from."

Q: "What's the company culture like?"

A: "The company culture is, well, fast-paced with almost a flat hierarchy - which is awesome! If you are looking for a place which allows you to move fast and collaborate on ideas on a daily basis - you should consider Viqal! Another good part of an early start-up is that we are still improving our processes - so if you like taking the initiative and don’t want to get bogged down in never-changing process cycles, send us your CV!"

Q: "Why did you go into emotional AI?"

A: "Well, emotional AI allows for a better overall service at restaurants, hotels, etc. I have had both bad and super awesome experiences at restaurants and hotels and if there is a way to measure and improve these experiences, then that is a goal worth pursuing. It’s also a very ambitious technical challenge and working towards it is fun!"

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Q: "What’s the biggest pain in the ass?"

A: "Well, the coolest thing and the biggest pain in the ass are the same thing here - the real-time collection, processing and visualisation. Making sure that all the building blocks of the extensive pipeline from the physical sensor to the data dashboard work is a technical challenge and sometimes it takes time until you find exactly where the problem originates. We are constantly improving our deployments - making them more modular and transparent, but it is for sure one of the biggest challenges still."

Q: "What will be the biggest challenge?"

A: "At this point, we are still a fairly small start-up and so scaling our technical solutions is also fairly straight-forward. It will be an interesting obstacle once we have to scale to thousands of physical sensors, all sending emotional data. It’s important to think about scaling before it becomes a problem."

Q: "How do you see the future of emotional AI?"

A: "The future of emotional AI is exciting! Being able to more accurately and continuously measure customer satisfaction means we can improve services and make sure we provide the experience customers enjoy! Smart spaces is the ultimate goal!"Thanks Nikolay!

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