MLOps Engineer
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with the organization of the first Data Science UA conference, setting the foundation for our growth. Over the past 9 years, we have diligently fostered the largest Data Science Community in Eastern Europe.
About the client:
Our client is a large-scale financial project with an extensive network and a strong market presence. The company manages vast amounts of financial data and is focused on enhancing its data infrastructure to support innovative solutions. With a commitment to long-term development, the team works on complex, high-impact projects — from optimizing data pipelines to implementing modern technologies across both cloud and on-premises environments. This is an opportunity to join a dynamic, data-driven company that values technical expertise and encourages growth.
About the role:
We are looking for a talented and passionate MLOps Engineer to join the team.
Requirements:
– 5+ years of experience in DevOps/Cloud Engineering.
– 3+ years of experience working with ML infrastructure.
– Deep understanding of the AWS ecosystem.
– Proven experience in developing scalable systems.
– Deep knowledge of AWS and experience with relevant services.
– Experience in deploying infrastructure for on-premises ML processes.
– Expertise in configuring and optimizing cloud resources.
– Experience with versioning systems for ML models and artifacts.
– Knowledge of the principles of organizing ML registries and data stores.
– Experience in configuring ML process monitoring systems.
– Understanding of the principles of optimizing ML models (experience with PyTorch is preferred) and cloud infrastructure security principles and best practices.
– Proficient in Python for automation and scripting.
– Experience with containerization (Docker), orchestration, monitoring and logging systems.
– Experience in configuring IAM and access management.
Nice to have:
– Experience in optimizing and compressing PyTorch models.
– AWS certifications (Solutions Architect, DevOps Engineer).
– Experience working with distributed training systems.
Responsibilities:
– Design and implement infrastructure to scale ML teams.
– Develop and maintain ML artifact management system (models, data, experiments).
– Configure and optimize on-premises and cloud infrastructure for ML processes.
– Work with Data Engineer to implement and optimize function repository.
– Ensure security and reliability of ML infrastructure.
– Participate in developing team’s technical infrastructure development strategy.
The company offers:
– Medical insurance;
– Laptop and cloud services by the company;
– Paid training, the opportunity to participate in the creation and publication of scientific articles (open-source knowledge development);
– Social package (paid sick leaves and vacation).
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