Lead MLOps Engineer (ID 61379) (вакансія неактивна)

WE ARE
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!

YOU ARE
A Machine Learning Engineer who is interested in operationalizing ML pipelines and bringing them to production. You will help us to 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 software engineering, have a solid knowledge of Machine Learning/Deep Learning models and workflows, 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
• 4+ years of relevant experience as ML Engineer or similar
• Solution architecture design and ability to articulate complex architectures to a non-technical audience
• Hands-on experience in ML operationalization
• Strong knowledge of Python and traditional Python DS/ML stack
• Work expertise with container technology and orchestration platforms such as Kubernetes
• Knowledge of any major cloud platform such as GCP, AWS, Azure, or IBM
• Background of setting up CI/CD/CT pipelines
• Strong requirements gathering and estimation
• Upper-Intermediate English level or higher
Your extra power reveals in the following expertise
• Hands-on experience with Kubeflow, MLflow, or similar
• Hadoop ecosystem and Apache Spark
• Workflow orchestration platforms such as Airflow
• Designing and building feature stores
• Message queues and streaming platforms
• R, Java/Scala, Julia
• Edge AI

YOU WANT TO WORK WITH
An opportunity to
• Communicate with stakeholders to identify use cases, gather requirements, and set up expectations
• Guide Engineering and Data Science teams on ML systems production lifecycle
• Educate Product teams on best practices for putting ML systems in production
• Collaborate with Data Science teams on model operationalization strategies
• Cooperate closely with Product teams to deliver and operate ML systems
• Design and 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, system monitoring
• Design and implement automated deployment and integration of ML models
• Set up scalable monitoring systems for data pipelines and ML models
• Maintain ML pipelines in production

TOGETHER WE WILL
• Operationalize our clients’ AI solutions by leveraging best practices in DevOps, Machine Learning, and Solution Architecture
• 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
• Get access to 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