DevOps(ML / LLM Infrastructure)

Kyivstar.Tech is seeking a DevOps Engineer to design, build, and operate infrastructure for their LLM platform. This DevOps-first role focuses on maintaining reliable, scalable, and efficient ML infrastructure across GPU/TPU/CPU environments, including data pipelines, training, and inference systems.

• Design, build, and operate scalable ML infrastructure on GCP (GKE) supporting experimentation and production workloads for LLMs and NLP systems
• Manage Kubernetes-based environments: deployment, scaling, upgrades, and reliability across GPU/TPU/CPU pools
• Build and maintain CI/CD pipelines (GitHub Actions, Jenkins) to automate testing, training, and deployment of ML services
• Implement infrastructure as code (Terraform, Ansible) to provision and manage cloud resources securely and cost-efficiently
• Ensure observability of ML systems through monitoring, logging, and alerting for infrastructure and production inference workloads
• Collaborate with ML Engineers and Data Engineers to design reliable training and inference pipelines
• Optimize resource utilization and cost of training and serving infrastructure
• Troubleshoot and resolve issues across the ML platform from data pipelines to distributed training and deployments
• Contribute to engineering best practices through code reviews and continuous improvement

• Experience: 4+ years in DevOps, Platform Engineering, or ML Infrastructure roles with strong understanding of production systems and distributed workloads
• Cloud & Infrastructure: Hands-on experience with GCP; experience with other major cloud platforms is a plus; strong understanding of cloud-native architectures
• Kubernetes & Containers: Solid experience with Docker and Kubernetes (preferably GKE); familiarity with Helm and Kubernetes networking
• CI/CD & Automation: Experience building and maintaining CI/CD pipelines (GitHub Actions, Jenkins, or similar)
• Workflow Orchestration: Experience with Airflow or similar tools
• Infrastructure as Code: Strong experience with Terraform (preferred) or similar tools
• Programming: Strong hands-on experience with Bash and/or Python scripting languages
• Observability & Reliability: Experience with monitoring and logging systems (Prometheus, Grafana); understanding of reliability and debugging in distributed systems
• ML Infrastructure: Familiarity with ML lifecycle and experience supporting ML workloads in production environments
• Collaboration: Ability to work closely with ML Engineers and Data Engineers

Work Arrangement: Office or remote - your choice

Benefits & Perks:

• Remote onboarding
• Performance bonuses
• Training and learning opportunities through company library, internal resources, and partner programs
• Health and life insurance
• Wellbeing program and corporate psychologist
• Reimbursement of expenses for Kyivstar mobile communication