Transforming the way thousands of global organizations do business by developing the most innovative technologies and processes in Big Data, Internet of Things (IoT), Data Science, and Experience Design.
We are one of the largest teams in Eastern Europe that stood at the origins of Data Science, so you will get tons of experience while working with the best talents in the field.
In a Data Science Center of Excellence, you will have a chance to contribute to a wide range of projects in different areas and technologies.
We are looking for a person who is inspired by data, analytics, and AI as much as we are, and who wants to grow with us!
A Machine Learning Engineer who is interested in operationalizing ML pipelines and bringing them to production. You will design and implement ML end-to-end solutions, create data pipelines and architectures, set up the infrastructure, and optimize existing models.
You should be strongly competent in Machine Learning / Deep Learning models and model lifecycle, have a solid knowledge of software engineering, and a good understanding of DevOps / MLOps principles.
A candidate should demonstrate such experience and abilities as
MS degree in computer science or related field 3+ years of relevant experience
Strong knowledge of Python and traditional Python DS / ML stack
Solid knowledge of ML solutions design patterns
Working knowledge of AI / ML and Data Engineering tools in any major cloud platform such as GCP, AWS or Azure
Hands-on experience with ML operationalization
Experience in setting up CI / CD / CT pipelines for ML
Hands-on experience with Kubeflow, MLflow, or similar
Working experience with containers and container orchestration platforms such as Kubernetes
Upper-Intermediate English level or higher
Strong requirements gathering and estimation
Your extra power is having experience with
Designing and building feature stores
Hadoop ecosystem and Apache Spark
Workflow orchestration platforms such as Airflow
Message queues and streaming platforms
Java / Scala
YOU WANT TO
Communicate use cases, requirements, and expectations with stakeholders
Guide Engineering and Data Science teams on ML systems production lifecycle
Collaborate with Data Science teams on model operationalization strategies
Work closely with Product teams to deliver and operate ML systems
Implement end-to-end production pipelines for ML solutions
Support and continuously enhance ML software infrastructure : CI / CD, data stores, cloud services, network configuration, security, and system monitoring
Set up scalable monitoring systems for data pipelines and ML models
TOGETHER WE WILL
Operationalize our clients’ AI solutions by leveraging best practices in Machine Learning and MLOps, DevOps, and Software engineering
Maintain synergy of Data Scientists, DevOps team, and ML Engineers to build infrastructure, set up processes, productize machine learning pipelines, and integrate them into existing business environments
Participate in international events
Get certifications in cutting-edge technologies
Have the possibility to work with the latest modern tools and technologies on different projects
Access strong educational and mentorship programs
Communicate with the world-leading companies from our logos portfolio
Work as a consultant on different projects with a flexible schedule