Infrastructure Architect defining and evolving cloud - centric infrastructure platforms. Collaborating with leadership and teams to ensure secure, scalable, and resilient solutions for Equitable Bank.
About the role
- AI Infrastructure Engineer at Xsolla designing AI/ML solutions for multi-cloud infrastructure. Collaborating on automation workflows and observability systems for improved infrastructure management.
Responsibilities
- 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)
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