Position: Software Engineer / Machine Learning Engineer
Category: Data Science
Experience: 9 years | English: fluent

In 2019 I was invited to the newly assembled team of ML engineers in a startup. Our task was to automate a big part of routine work that usually is done by data analysts. No one in our team had experience in the domain by that time, and we had to start the project entirely from scratch. I led the team through all stages of a project: starting from domain knowledge transfer and first research till integration of models' inference with data analysis tool and supporting models on production. As a result, the time spent by analysts doing their regular work decreased three times. As a side effect, the quality of analysts' work significantly improved because they started focusing on important data patterns omitting the most routing and tedious part of their work.

In my next journey, I was invited into a project in which research kind of work was already done and at that time we have to go into productions with a few hundred models. The main challenge was to organize previous research work in clean efficient production-ready code and set up engineering pipelines in a way, where we can simultaneously train dozens of models and receive feedback (performance metrics) as soon as possible. As a result, we decreased all models' training time from around 24 hours to 30 minutes.
I'm an ML engineer who has both Machine Learning and infrastructure skills (I have Kubernetes certification, for example).

I have 9+ years of experience in IT in different roles: DevOps engineer (5 years), Python Back-end engineer (4 years), ML engineer (2+ years), Technical Lead (1 year). Such broad experience makes it possible for me to speak the same language with different engineers and understand them, which is very important while collaborating inside a team or a company. As an ML engineer, I'm able to not only train models in Jupyter notebook but also know how to convert business goals into technical tasks, design, and build the whole production system with ML models and surrounding infrastructure. Besides that, I have strong math and statistical background, and terms like "Central Limit Theorem", "p-value", "probability distribution" wouldn't scare me at all. I mean that I'm confident in doing complicated data analyses and sophisticated feature engineering.

I have experience in building two big software projects from scratch. One of them is the ML project, where I had a Tech Lead role (leading a team of 4 ML engineers). On that project, I designed the whole system (ML models, data pipelines, monitoring, analysis tools) and implemented it with the team from the first commit in git till the continuous deployments on production.
The most important thing for me is the people, i.e., the coworkers. I want to work in a team of passionate ML practitioners and software engineers who are cheerful, mature, and love their work — people who have an outstanding experience and who love to share it with others and support others.

The ideal management, for me, is clear, transparent, with common sense in the first place, without micro-management, time tracking, etc. I like when managers treat people as mature adult individuals and assess their work by the results.

Ideally, expect to have an opportunity with all above mentioned and where my broad experience is needed.