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Lead DevOps/MLOps Engineer

Remote in Belarus, & 4 others
Data DevOps& 7 others
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We are seeking a driven and highly skilled Lead DevOps/MLOps Engineer to assess client infrastructure to enable the deployment of Enterprise Data Platform (EDP) components, aiming to ensure seamless integration and operational efficiency for a leading company in the oil and gas industry. The focus is on evaluating existing systems and preparing them for a scalable data platform rollout.

Responsibilities
  • Conduct comprehensive assessments of client infrastructure for EDP rollout feasibility
  • Identify and document infrastructure gaps and recommend remediation actions
  • Collaborate with data engineering and DevOps teams to align deployment strategies
  • Develop and maintain deployment automation scripts and tools
  • Coordinate with third-party vendors to ensure compatibility and support
  • Monitor infrastructure performance and provide feedback for optimization
  • Support continuous improvement of deployment processes and infrastructure standards
Requirements
  • 5+ years in DevOps / Data Engineering, including technical or team leadership experience
  • Expertise in infrastructure assessment and readiness evaluation for data platform deployments
  • Proficiency in DevOps practices tailored to data engineering environments
  • Background in deploying platform and application workloads on Kubernetes
  • Knowledge of EDP components and their deployment requirements, along with CI/CD and Argo
  • Ability to analyze complex client environments and identify potential integration challenges
  • Experience collaborating within cross-functional teams and coordinating with external vendors
  • English proficiency at an Upper-Intermediate level (B2) or higher
Nice to have
  • Familiarity with MLflow, Airflow, and automated retraining
  • Skills in data quality and monitoring tools
  • Competency in Docker
  • Experience with Linux
  • Knowledge of Python
  • Understanding of machine learning and agentic AI applications, and what it takes to deploy and run them reliably in production