Infrastructure Engineering Manager

As an Infrastructure Engineering Manager at Unifonic, you will lead the engineering and operations of production cloud platforms, combining Infrastructure Engineering, Site Reliability Engineering (SRE), Cloud Platform Engineering, and AI-native engineering to ensure reliability, scalability, security, performance, and automation. You will remain hands-on by reviewing infrastructure code and architecture, troubleshooting complex production issues, working with Kubernetes and cloud services, and using AI engineering tools such as Claude Code daily. You will focus on Google Cloud Platform (GCP) and Oracle Cloud Infrastructure (OCI), with strong practical GCP expertise to support the ongoing migration to GCP, while collaborating with the Infrastructure Engineering Manager – Developer Experience, who is responsible for non-production environments, CI/CD, and developer tooling. Help us shape the future of communication by: • Owning the architecture, engineering, operation, and continuous improvement of production infrastructure across GCP and OCI. • Taking a leading technical role in migrating production workloads to GCP. • Designing highly available, scalable, resilient and secure production platforms. • Setting engineering standards for compute, networking, storage, IAM and cloud-native services. • Making sure the architecture meets availability, performance, capacity and disaster recovery requirements. • Establishing consistent engineering patterns across GCP and OCI instead of treating each cloud as an isolated platform. • Owning production Kubernetes platforms and their supporting ecosystem. • Keeping clusters secure, observable, scalable, resilient and automated. • Owning platform capabilities including ingress, service networking and service mesh, monitoring and logging. • Working with application engineering teams to improve workload reliability and operational readiness. • Building automation and self-service capabilities that reduce manual infrastructure work. • Establishing and improving SRE practices across production infrastructure. • Driving reliability through meaningful SLIs, SLOs, and operational metrics. • Improving availability, capacity management and production resilience. • Leading infrastructure-level troubleshooting during complex production incidents. • Championing rigorous root-cause analysis and permanent corrective actions. • Reducing operational toil through engineering and automation. • Improving monitoring, observability, and early detection of infrastructure problems. • Building reliability into architecture and design, not only after incidents. • Driving an Infrastructure-as-Code-first operating model. • Keeping strong discipline around Terraform, reusable modules, automated validation and controlled production changes. • Making sure infrastructure changes follow software engineering practice: source control, pull requests, peer review, testing and automated deployment. • Using Bitbucket as the core of the infrastructure engineering workflow. • Holding infrastructure repositories to the same standards as production application code. • Embedding AI into daily infrastructure engineering, not as an occasional add-on. • Using Claude Code to understand repositories, develop and review infrastructure code, investigate problems and automate workflows. • Building and using MCP (Model Context Protocol) servers that connect AI agents to infrastructure tooling. • Applying AI to infrastructure monitoring, optimisation, incident investigation and root-cause analysis. • Using AI to develop, validate and review Terraform. • Defining where human validation and approval remain essential. • Making sure AI is used safely around production systems, credentials and sensitive operational data. Oversee critical production platform and backing services, including their lifecycle, standardisation, automation, availability, security, backup and disaster recovery: • Databases and data infrastructure (for example MySQL, PostgreSQL) • Caching (for example Redis) • Messaging and middleware • Kubernetes platform services • Networking and connectivity • IAM and secrets management • Observability platforms • Backup and recovery services • Build security into infrastructure design and day-to-day engineering. • Own IAM, network security, segmentation, secrets management and infrastructure hardening. • Partner with Security Engineering on vulnerabilities and production risks. • Make sure AI-assisted engineering follows security and governance controls. • Keep infrastructure changes auditable through Infrastructure as Code and source control. • Put guardrails and human oversight on production automation. • Lead, mentor and grow a high-performing team of Infrastructure and SRE engineers. • Stay technically credible: guide architecture, review solutions and join complex troubleshooting. • Build a culture of engineering ownership rather than ticket handling. • Challenge manual processes and find opportunities to automate. • Drive adoption of AI-native engineering and coach engineers on Claude Code, MCP tooling and AI-assisted workflows. • Partner with Product Engineering, Security and other teams on production platform strategy. • Set clear engineering standards and keep documentation and knowledge sharing strong. What you’ll bring: • 10+ years of experience across Infrastructure, Cloud, Platform Engineering, DevOps, or SRE. • 3+ years of experience leading infrastructure, SRE, platform, or cloud engineering teams. • Proven experience designing and operating highly available, production-grade cloud environments. • Strong hands-on experience with production Kubernetes. • Significant hands-on experience with Terraform and Infrastructure as Code (IaC). • Strong understanding of networking, IAM, security, observability, and production operations. • Experience leading a major infrastructure migration or platform transformation. • Substantial hands-on experience designing, deploying, troubleshooting, and operating production workloads on Google Cloud Platform (GCP). • Ability to discuss real-world GCP production architecture and operational decisions beyond theory or certifications. • Hands-on experience with Oracle Cloud Infrastructure (OCI) and the ability to operate across both OCI and GCP. • Daily, hands-on use of Claude Code in real engineering work. • Practical understanding and use of MCP (Model Context Protocol) servers. • Proven use of AI in software engineering, SRE, or infrastructure workflows beyond chat-style prompting. • Ability to use AI effectively across large infrastructure and code repositories. • Experience using AI to generate, understand, review, and troubleshoot Infrastructure as Code. • Experience using AI for production troubleshooting and incident investigation. • Familiarity with agentic engineering workflows, including sub-agents, reusable skills, and commands. • Strong judgment in validating AI-generated code and keeping humans involved in critical decisions. • Strong Git-based engineering practices using Bitbucket. • Ability to demonstrate practical AI-native engineering workflows. • Knowledge of Kubernetes, Helm, Kubernetes Operators, and service mesh. • Proficiency in Git, Bitbucket, and Linux administration. • Strong understanding of cloud networking, DNS, IAM, and secrets management. • Experience with observability, monitoring, and logging. • Experience with CI/CD, GitO

10+ years of experience across Infrastructure, Cloud, Platform Engineering, DevOps, or SRE. 3+ years of experience leading infrastructure, SRE, platform, or cloud engineering teams. Proven experience designing and operating highly available, production-grade cloud environments. Strong hands-on experience with production Kubernetes. Significant hands-on experience with Terraform and Infrastructure as Code (IaC). Strong understanding of networking, IAM, security, observability, and production operations. Experience leading a major infrastructure migration or platform transformation. Substantial hands-on experience designing, deploying, troubleshooting, and operating production workloads on Google Cloud Platform (GCP). Ability to discuss real-world GCP production architecture and operational decisions beyond theory or certifications. Hands-on experience with Oracle Cloud Infrastructure (OCI) and the ability to operate across both OCI and GCP. Daily, hands-on use of Claude Code in real engineering work. Practical understanding and use of MCP (Model Context Protocol) servers. Proven use of AI in software engineering, SRE, or infrastructure workflows beyond chat-style prompting. Ability to use AI effectively across large infrastructure and code repositories. Experience using AI to generate, understand, review, and troubleshoot Infrastructure as Code. Experience using AI for production troubleshooting and incident investigation. Familiarity with agentic engineering workflows, including sub-agents, reusable skills, and commands. Strong judgment in validating AI-generated code and keeping humans involved in critical decisions. Strong Git-based engineering practices using Bitbucket. Ability to demonstrate practical AI-native engineering workflows. Knowledge of Kubernetes, Helm, Kubernetes Operators, and service mesh. Proficiency in Git, Bitbucket, and Linux administration. Strong understanding of cloud networking, DNS, IAM, and secrets management. Experience with observability, monitoring, and logging. Experience with CI/CD, GitOps.

Own the architecture, engineering, operation, and continuous improvement of production infrastructure across GCP and OCI. Lead technical migration of production workloads to GCP. Design highly available, scalable, resilient and secure production platforms. Set engineering standards for compute, networking, storage, IAM and cloud-native services. Ensure architecture meets availability, performance, capacity and disaster recovery requirements. Establish consistent engineering patterns across GCP and OCI. Own production Kubernetes platforms and supporting ecosystem. Keep clusters secure, observable, scalable, resilient and automated. Own platform capabilities including ingress, service networking and service mesh, monitoring and logging. Work with application engineering teams to improve workload reliability and operational readiness. Build automation and self-service capabilities to reduce manual infrastructure work. Establish and improve SRE practices across production infrastructure. Drive reliability through meaningful SLIs, SLOs, and operational metrics. Improve availability, capacity management and production resilience. Lead infrastructure-level troubleshooting during complex production incidents. Champion rigorous root-cause analysis and permanent corrective actions. Reduce operational toil through engineering and automation. Improve monitoring, observability, and early detection of infrastructure problems. Build reliability into architecture and design, not only after incidents. Drive an Infrastructure-as-Code-first operating model. Keep discipline around Terraform, reusable modules, automated validation and controlled production changes. Ensure infrastructure changes follow software engineering practice: source control, pull requests, peer review, testing and automated deployment. Use Bitbucket as the core of the infrastructure engineering workflow. Hold infrastructure repositories to the same standards as production application code. Embed AI into daily infrastructure engineering, including Claude Code, MCP tooling and AI-assisted workflows. Build and use MCP servers that connect AI agents to infrastructure tooling. Apply AI to infrastructure monitoring, optimisation, incident investigation and root-cause analysis. Use AI to develop, validate and review Terraform. Define where human validation and approval remain essential. Ensure AI is used safely around production systems, credentials and sensitive operational data. Oversee critical production platform and backing services, including lifecycle, standardisation, automation, availability, security, backup and disaster recovery. Partner with Security Engineering on vulnerabilities and production risks. Make sure AI-assisted engineering follows security and governance controls. Keep infrastructure changes auditable through Infrastructure as Code and source control. Put guardrails and human oversight on production automation. Lead, mentor and grow a high-performing team of Infrastructure and SRE engineers. Stay technically credible: guide architecture, review solutions and join complex troubleshooting. Build a culture of engineering ownership rather than ticket handling. Challenge manual processes and find opportunities to automate. Drive adoption of AI-native engineering and coach engineers on Claude Code, MCP tooling and AI-assisted workflows. Partner with Product Engineering, Security and other teams on production platform strategy. Set clear engineering standards and keep documentation and knowledge sharing strong.