AI Infrastructure Engineer

Role Overview

• Design and implement AI/ML-powered solutions for infrastructure use cases, including predictive autoscaling, anomaly detection, intelligent cost optimization, and automated remediation across GCP and multi-cloud environments
• Build and maintain AI-driven monitoring and observability systems that correlate logs, metrics, and traces to surface root causes, predict bottlenecks, and reduce mean time to resolution (MTTR)
• Develop and operate automated incident response workflows using AI-powered playbooks that diagnose, contain, and resolve infrastructure issues with minimal manual intervention
• Integrate AI tooling into CI/CD pipelines to improve deployment reliability, automate test prediction, score release health, and support rollback automation
• Contribute to the development of internal AI agents and virtual assistants integrated into developer workflows (Slack, IDEs, Confluence) — enabling self-service for provisioning, troubleshooting, and infrastructure guidance
• Implement AI/ML-based anomaly detection and automated vulnerability management workflows to enhance the security posture of Xsolla's infrastructure
• Prototype and productionize Generative AI solutions for infrastructure automation, including auto-generation of Terraform/Puppet modules, IaC configurations, runbooks, and change documentation
• Collaborate with senior engineers and leadership to evolve and execute the infrastructure AI strategy across its implementation phases
• Maintain clear documentation of AI tools, integrations, and automated workflows; share knowledge and best practices across the team

Requirements

• 5–7 years of experience in infrastructure engineering, DevOps, SRE, or a related field
• Hands-on experience with GCP (priority) and/or AWS; solid understanding of cloud resource management, scaling, and cost structures
• Practical experience building or integrating AI/ML-powered tools in an operational context (anomaly detection, predictive models, LLM-based automation, or similar)
• Experience with infrastructure-as-code tools — Terraform, Puppet, Ansible, or equivalent
• Proficiency in Python for scripting, automation, and AI/ML integration; Bash or Go a plus
• Working knowledge of Kubernetes and container orchestration in production environments
• Familiarity with observability and monitoring stacks (Prometheus, Grafana, ELK, Datadog, or similar)
• Familiarity with LLM APIs (OpenAI, Anthropic, or similar) and prompt engineering for operational use cases
• Strong problem-solving mindset with a bias toward automation and eliminating toil
• Fluent in English (written and verbal).

Tech Stack

• Ansible
• AWS
• Cloud
• Google Cloud Platform
• Grafana
• Kubernetes
• Prometheus
• Puppet
• Python
• Terraform
• Go