Senior DevOps/MLOps Engineer

This is a fully remote position, open to applicants in Poland.

📋 Description

• Create and execute scalable, secure, and cost-efficient MLOps solutions on cloud platforms.

• Streamline deployment pipelines to minimize manual intervention.

• Work closely with data scientists to ensure solutions align with MLOps architecture and industry best practices.

• Embed security measures throughout the machine learning lifecycle.

• Oversee issues from initial root cause analysis to resolution, providing feedback to prevent future occurrences.

• Contribute to the design of system architecture and software.

⛳️ Requirements

• Expected work duration of 6 months.

• Highly self-motivated with the capability to independently identify stakeholders and drive tasks to completion.

• Strong skills in stakeholder management, with the ability to interact effectively across various levels of seniority.

• A curious mindset focused on continuous improvement and the willingness to challenge existing processes.

• Excellent communication skills to facilitate effective collaboration with cross-functional teams.

• Strong time management abilities, demonstrating a high degree of organization and dependability.

• Proficiency in the Russian language is essential.

• Experience with AWS architecture, security best practices, and cost optimization strategies.

• Proficient in Databricks.

• Familiarity with cloud-managed ML platforms such as AWS Sagemaker or Google Vertex AI.

• Expert knowledge of Terraform or Terragrunt for managing multi-cloud infrastructure.

• Strong expertise in Kubernetes, including cluster scaling and advanced networking concepts.

• Practical experience with observability tools such as Prometheus, Grafana, Loki, or ELK.

• Comprehensive understanding of Git-based workflows and CI/CD tools like ArgoCD or FluxCD.

• Strong grasp of Docker security and container orchestration principles.

• Advanced skills in MLOps for continuous retraining and deployment processes.

• Experience with ML pipeline tools such as Kubeflow or Argo Workflow.

• Familiarity with LLMOps frameworks like Langfuse, ollama, or vLLM.

🏝️ Benefits

• Opportunities for professional development.

• Flexible working arrangements.