— No less than 4 years of relevant experience as ML Engineer;
— Strong knowledge of ML theory and practice — pros & cons of different model types, validation, metrics, etc;
— Experience in text analysis, NLP models, particularly transformer based models like GPT, BERT and experience with text similarity models and text summarization problem. Knowledge of topic models will be a bonus;
— Strong knowledge of Python;
— Hands-on experience with scientific Python toolkit: numpy, pandas, scikit-learn, jupyter-tools, seaborn, plotly, etc.;
— Solid knowledge of SQL, Docker, Cloud engineering;
— Knowledge of statistics, probability theory and linear algebra;
— MSc/BSc in Computer Science or Data Science or similar degree is a plus;
— Good verbal and written English communication skills (Intermediate level or higher).
— Challenging and exciting work in a friendly and professional environment;
— Excellent career opportunities and a competitive salary;
— Professional development program with internal seminars on technical topics;
— Flexible work schedule and pleasant working environment including modern and comfortable office;
— 15 working days of paid vacation (21 calendar days), 5 half-paid sick leave days;
— Annual fund for covering employees’ personal needs (medical insurance, sport activities etc.);
— Free English courses;
— Great corporate events and celebrations;
— Access to the corporate library and much more.
— Design and implement ML models and solutions;
— Develop machine learning pipelines and proof-of-concept prototypes for a variety of use cases;
— Monitor the performance of deployed ML models over time with the help of already developed metrics. Your own approach and tools are welcome in addition to the existing ones;
— Manage the whole machine learning lifecycle;
— Identify any possible flaws in model serving pipeline(s), look for root causes, fix if possible, involve other engineers when needed.
Our client’s solution is a platform for streamlining and automating DevOps operations and incident management. With its help, the engineering team can respond to incidents and outages directly from Slack. Automation simplifies building and maintaining applications and greatly accelerates product release timelines, and improves its overall quality. The platform integrates with over 200 services and tools, such as ServiceNow, Jira Service Management, and GitHub. It simplifies integration management with other apps weaving together APIs.
Our client’s modern tool helps an engineering team operate as a finely-tuned mechanism and achieve the following goals:
— Collecting data on the entire development process, analyzing it for insights, and monitoring the problem-solving process;
— Increasing uptime;
— Service request management without manual work and glue code
— Efficient handling of incidents