MLOps Lead
2 днів тому


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

  • 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

  • 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
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    Клацнувши по кнопці "# кнопка", я даю згоду neuvoo на обробку моїх даних та надсилання сповіщень електронною поштою, як це детально описано в Політиці конфіденційності neuvoo. Я можу будь-коли відкликати свою згоду або скасувати підписку.