DevOps/MLOps Engineer (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.

About us

Kyivstar.Tech is a Ukrainian hybrid IT company and a resident of Diia.City.

We are a subsidiary of Kyivstar, one of Ukraine’s largest telecom operators.

Our mission is to change lives in Ukraine and around the world by creating technological solutions and products that unleash the potential of businesses and meet users’ needs.

Over 600+ KS.Tech specialists work daily in various areas: mobile and web solutions, as well as design, development, support, and technical maintenance of high-performance systems and services.

We believe in innovations that truly bring quality changes and constantly challenge conventional approaches and solutions. Each of us is an adherent of entrepreneurial culture, which allows us never to stop, to evolve, and to create something new.

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