Infrastructure Engineering Manager

Role overview

Production platforms and reliability

• Own production infrastructure architecture, engineering, operations, and continuous improvement across GCP and OCI.
• Lead production migration to GCP and design highly available, scalable, resilient, secure platforms.
• Set standards for compute, networking, storage, IAM, cloud-native services, and consistent cross-cloud patterns.
• Own production Kubernetes and supporting capabilities, including ingress, service networking, service mesh, monitoring, and logging.
• Establish SRE practices, SLIs, SLOs, operational metrics, availability, capacity management, and production resilience.
• Lead complex incident troubleshooting, rigorous root-cause analysis, permanent corrective actions, and operational toil reduction.

Infrastructure engineering and AI

• Build automation and self-service capabilities; apply Infrastructure as Code practices using Terraform, reusable modules, validation, and controlled changes.
• Use source control, pull requests, peer review, testing, and automated deployment for infrastructure repositories, with Bitbucket as the core workflow.
• Use Claude Code and MCP to understand repositories, develop and review infrastructure code, investigate problems, and automate workflows.
• Apply AI to monitoring, optimization, incident investigation, root-cause analysis, and Terraform development, validation, and review.
• Define human validation and approval requirements and use AI safely around production systems, credentials, and sensitive operational data.

Services, security, and team leadership

• Oversee databases such as MySQL and PostgreSQL, Redis caching, messaging and middleware, Kubernetes services, networking, IAM, secrets, observability, backup, and recovery.
• Own IAM, network security, segmentation, secrets management, and infrastructure hardening; partner with Security Engineering on vulnerabilities and production risks.
• Lead, mentor, and grow Infrastructure and SRE engineers; guide architecture, review solutions, and join complex troubleshooting.
• Build engineering ownership, promote automation, and coach engineers on Claude Code, MCP, and AI-assisted workflows.
• Partner with Product Engineering, Security, and other teams on platform strategy; set standards and support documentation and knowledge sharing.

Experience and technical requirements

• At least 10 years of experience in infrastructure, cloud, platform engineering, DevOps, or SRE, including at least 3 years leading relevant teams.
• Proven experience operating production-grade, highly available cloud environments and leading a major infrastructure migration or platform transformation.
• Strong hands-on production Kubernetes and significant Terraform and Infrastructure as Code experience.
• Substantial hands-on GCP production workload experience and hands-on experience operating OCI and GCP.
• Strong understanding of networking, IAM, security, observability, and production operations.
• Daily hands-on Claude Code use, practical MCP experience, and proven AI-assisted engineering beyond chat-style prompting.
• Experience using AI across large repositories, for IaC review and troubleshooting, and for production incident investigation.
• Strong judgment validating AI-generated code and keeping humans involved in critical decisions.
• Strong Git and Bitbucket practices; knowledge of Helm, Kubernetes Operators, service mesh, Linux, CI/CD, GitOps, incidents, high availability, and disaster recovery.
• GCP professional certifications, other AI coding tools, MCP integrations, and telecommunications, CPaaS, or messaging experience are listed as additional relevant experience.