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