Responsibilities

• Design and implement scalable, secure, and cost-effective MLOps solutions
• Lead the delivery process of complex ML scenarios, including continuous retraining and validation
• Automate deployment pipelines and reduce manual toil
• Collaborate with data scientists to ensure solutions align with MLOps architecture and best practices
• Integrate security throughout the full machine learning lifecycle
• Manage issues from root cause to resolution, providing feedback to prevent recurrence
• Contribute to software system architecture and design
• Mentor and support team members, sharing knowledge and best practices

Qualifications

• Expert knowledge of Terraform/Terragrunt for multi-cloud infrastructure management
• Strong expertise in Kubernetes, including cluster scaling and advanced networking concepts
• Proficiency with Helm, including custom chart development
• Hands-on experience with observability stacks (Prometheus, Grafana, Loki, ELK)
• Deep knowledge of Git-based workflows and CI/CD integration (ArgoCD, FluxCD)
• Experience with AWS architecture, security best practices, and cost optimization
• Expert proficiency in Linux system administration and troubleshooting. Strong understanding of Docker security and container orchestration
• Advanced skills in MLOps for continuous retraining and deployment
• Familiarity with ML pipeline deployment tools (Kubeflow, Argo Workflow)
• Experience with LLMOps integration and related frameworks (Langfuse, ollama, vLLM)
• Proficiency with cloud-managed ML platforms like AWS Sagemaker or Google Vertex AI

Conditions & Benefits

• Stable salary, official employment
• Health insurance
• Hybrid work mode and flexible schedule
• Relocation package offered for candidates from other regions
• Access to professional counseling services including psychological, financial, and legal support
• Discount club membership
• Diverse internal training programs
• Partially or fully payed additional training courses
• All necessary work equipment