Position: Middle/Senior ML Engineer image Ukraine
Company: Provectus
Category: Data Science
Experience required: 2y
English level: intermediate

Join us if you have the same passion for making products using AI/ML technologies, cloud services, and data engineering.
Join us if you value a friendly corporate environment, and almost a family-driven approach to every employee.
Join us if you want to grow professionally and ready for learning new things constantly.
As an ML Engineer, you’ll be provided with all opportunities for development and growth.

What is required:

Comfortable with standard ML algorithms and underlying math;
Practical experience with solving classification and regression tasks in general, feature engineering;
Practical experience with ML models in production;
Practical experience with one or more use cases from the following: NLP, Computer Vision, Forecasting, Recommendation engines;
Solid software engineering skills (i.e., ability to produce well-structured modules, not only notebook scripts);
Python expertise, Docker;
Al least strong Intermediate English level.

Will be a plus:

Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda);
Practical experience with deep learning models;
Practical experience with machine learning pipelines to orchestrate complicated workflows;
Practical experience with Spark/Dask -is nice to have.

What we offer:

Opportunity to grow as an Architect;
Opportunity to create solutions in different ML areas;
Attractive professional and educational opportunities, a competitive salary, and fun colleagues who make every online and offline event a treat.

Responsibilities:

With our team of key ML Engineers perform a client-facing role in AI consulting;
Create ML models from scratch or improve existing models;
Collaborate with the engineering team, data scientists, and product managers to productionize models;
Develop experimentation roadmap;
Setup reproducible experimentation environment and implement experimentation pipelines.