DevOps(ML / LLM Infrastructure)

We are looking for a DevOps Engineer to design, build, and operate the infrastructure behind our LLM platform. You will be responsible for keeping our ML infrastructure reliable, scalable, and efficient — from data pipelines to training and inference.

In this role, you will develop and maintain CI/CD pipelines, orchestration workflows, and observability for distributed ML workloads across GPU/TPU/CPU environments.

This is a DevOps-first role with strong exposure to ML infrastructure. You will work closely with ML Engineers and Data Engineers, while focusing on building a robust, automated, and production-grade platform that accelerates model development and delivery.

Responsibilities:

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

Required Qualifications:

• 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. other major cloud platforms is a plus. Strong understanding of cloud-native architectures and experience designing scalable systems for compute and data-intensive workloads.
• Kubernetes & Containers: Solid experience with Docker and Kubernetes (preferably GKE), including deploying, scaling, and operating production workloads. Familiarity with Helm and Kubernetes networking fundamentals.
• CI/CD & Automation: Experience building and maintaining CI/CD pipelines (GitHub Actions, Jenkins, or similar) to automate testing, deployment, and infrastructure changes.
• Workflow Orchestration: Experience with Airflow (or similar tools).
• Infrastructure as Code: Strong experience with Terraform (preferred) or similar tools for provisioning and managing infrastructure in a reproducible way.
• Programming: Strong hands-on scripting languages experience (Bash and/ or Python).
• Observability & Reliability: Experience with monitoring and logging systems (e.g., Prometheus, Grafana). Understanding of reliability, alerting, and debugging in distributed systems.
• ML Infrastructure Understanding: Familiarity with the ML lifecycle (training, evaluation, inference) and experience supporting ML workloads in production environments.
• Collaboration: Ability to work closely with ML Engineers and Data Engineers, translating ML requirements into reliable and scalable infrastructure solutions.

What we offer:

• Office or remote — it’s up to you.
• Remote onboarding
• Performance bonuses
• We train employees with the opportunity to learn through the company’s library, internal resources, and programs from partners
• Health and life insurance
• Wellbeing program and corporate psychologist
• Reimbursement of expenses for Kyivstar mobile communication