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 best and oldest Data Science teams in Ukraine and you will get tons of experience working with the best talents in the field.
We are a Data Science Center of Excellence and you will have a chance to contribute to a wide range of projects from different areas and technologies.
We’re looking for you, a person who is inspired by data, analytics, and AI as we do, and who wants to grow with us!
MLOps Engineer with MS degree in computer science or related field and 4+ years of relevant experience
Experienced with solution infrastructure design
ExperiencedinbuildingMachine Learningsystems and workflows, esp. model operationalization
Experienced with engineering best practices, including analyzing, designing, developing, deploying, and supporting solution infrastructure implementations and upgrades
Experienced in GCP, AWS or Azure beyond basic provisioning, especially data pipeline components, CI / CD, k8s, and ML-relevant managed services (certification)
Experienced with container technology and orchestration platforms such as Kubernetes
Experienced with building CI / CD pipelines using Gitlab, Jenkins, Azure DevOps, Google Cloud Build, or similar
Skilled in Python, SQL, and Shell scripting
Demonstrating Upper-Intermediate English level or higher (oral / written)
The one who possesses excellent organization, time-management, presentation, and communication skills
Strong in requirement gathering and estimation
Able to articulate complex architecture to non-technical audiences
Your extra power reveals in the following
Hands-on experience with Kubeflow, MLFlow, or similar
Experience with workflow orchestration platforms such as Airflow
Experience with databases and object stores such as PostgreSQL, Athena, BigQuery, S3or similar
Experience in feature store design Experience with Hadoop ecosystem, Apache Spark, or similar
Experience with stream-processing platforms such as Apache Kafka, Cloud Pub / Sub
Experience developing solutions using automation tools such as Terraform, CloudFormation, etc. (optional)
Experience with monitoring tools such as Grafana, Prometheus, Stackdriver, CloudWatch, etc.
Familiarity with GitOps approach and tools
Experience with hybrid cloud solutions
YOU WANT 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
Work 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, etc.
Design and implement automated deployment and integration of ML models
Setup 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 on cutting-edge technologies
Have the ability to work with the latest modern tools and technologies on different projects
Have 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