Job Details

Building the machine learning production infrastructure (or MLOps) is the biggest challenge most large companies currently have in making the transition to becoming an AI-driven organization. This position is an opportunity for an experienced, server-side developer to build expertise in this exciting new frontier.


  • Design the data pipelines and engineering infrastructure to support our clients’ enterprise machine learning systems at scale
  • Take offline models data scientists build and turn them into a real machine learning production system
  • Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
  • Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients’ machine learning systems
  • Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
  • Support model development, with an emphasis on auditability, versioning, and data security
  • Facilitate the development and deployment of proof-of-concept machine learning systems
  • Communicate with clients to build requirements and track progress


  • Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)
  • Strong software engineering skills in complex, multi-language systems
  • Fluency in Python
  • Comfort with Linux administration
  • Experience working with cloud computing and database systems
  • Experience building custom integrations between cloud-based systems using APIs
  • Experience developing and maintaining ML systems built with open source tools
  • Experience developing with containers and Kubernetes in cloud computing environments
  • Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)
  • Ability to translate business needs to technical requirements
  • Strong understanding of software testing, benchmarking, and continuous integration
  • Exposure to machine learning methodology and best practices
  • Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)

Education & Experience

  • 2–5 years experience building production-quality software.
  • Bachelors or Masters degree and/or equivalent professional experience
  • Professional communications skills with high proficiency in Lithuania and English

Job Overviews

  • Location:


  • Job Title:

    ML DevOps Engineer

  • Hours:

    48h / week

  • Rate:

    €18.23 - €28.65 / hour

  • Salary:

    €35k - €55k netto

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