• Develop, maintain and evolve CI/CD pipelines, ensuring continuous, stable and secure deliveries • Automate infrastructure and application deployment processes, reducing toil and increasing reliability • Continuously monitor and optimize the performance, availability and security of production environments • Administer and support AWS cloud environments, ensuring resilience and scalability • Serve as a technical reference for the development team, promoting best practices for continuous delivery • Ensure end-to-end observability with robust practices for metrics, logs, tracing, versioning and rollback • Manage and ensure availability and performance of MongoDB and PostgreSQL databases • Act as a FinOps mentor and point of reference, fostering a culture of cloud cost efficiency and governance • Lead the response to critical incidents — rapidly diagnose issues, coordinate resolution and ensure clear communication during crises • Conduct blameless post-mortems, turning incidents into lessons and concrete improvements

Role Description 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. 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. Benefits - 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.

• Manage and maintain cloud infrastructure on Azure, including Azure Kubernetes Service (AKS) clusters and supporting resources • Build, improve, and maintain CI/CD pipelines using GitHub Actions to support reliable and repeatable deployments • Own and enhance our Grafana implementation; designing dashboards, configuring alerts, and supporting incident management workflows • Monitor system health, triage incidents, and drive root cause analysis to prevent recurrence • Collaborate with development teams to define and track SLIs, SLOs, and error budgets that align with business goals • Contribute to infrastructure-as-code practices using Pulumi • Identify and resolve reliability risks through capacity planning, performance tuning, and proactive system improvements • Participate in an on-call rotation to support production systems and respond to incidents • Document runbooks, operational procedures, and architectural decisions to support team knowledge sharing

• Build and maintain infrastructure as code using Ansible, Docker, and cloud services (AWS), treating infrastructure like a software project — version-controlled, tested, and reviewable • Develop internal tools and automation in Python or Bash to streamline deployments, monitoring, and data workflows • Own CI/CD pipelines — set up, improve, and maintain continuous integration and deployment processes so our teams can ship with confidence • Manage and optimize databases (MySQL) including backups, performance tuning, and query optimization • Operate and improve our Linux-based server infrastructure, including our on-premise Proxmox environment and cloud resources • Implement monitoring, logging, and alerting to ensure high availability and fast incident response (e.g. Grafana, NewRelic) • Strengthen security and compliance — from firewall management and system hardening to access control and update policies • Collaborate closely with Engineering, Data Science, and Product to support data pipelines, internal tools, and deployment workflows