Lead Google Cloud Platform DevOps Engineer

We are looking for a seasoned Lead Google Cloud Platform DevOps Engineer to take on a pivotal role in delivering Google Cloud Platform solutions for our clients. In this leadership position, you will drive the architecture, guide teams on best practices, and provide strategic oversight for cloud-native transformations. You will play a key role in shaping technical decisions, managing complex deployments, and establishing processes for scalability and reliability. If you have the expertise and passion for leading impactful projects, we invite you to apply.

We would like to inform you that this role is primarily offered under a standard employment contract, in accordance with applicable labor regulations.

Responsibilities Take ownership of clarifying technical requirements and defining project scope Lead customer engagements to ensure project alignment and success Direct and supervise the adaptation of customer applications into a cloud-native framework Define and implement DevOps practices, including CI/CD pipelines and Infrastructure as Code (IaaC) Architect and optimize cloud applications for scalability, covering CDN, caching, compute optimizations, and robust load-balancing setups Oversee deployment and configuration of Google Cloud Platform services, including identity management, secure network architecture, application security, and billing processes Create and maintain thorough documentation of technical decisions, designs, and processes Monitor, troubleshoot, and ensure optimal performance of cloud applications, including Kubernetes clusters and network connectivity Mentor and guide team members via knowledge-sharing and training sessions to strengthen technical expertise across the team Requirements 5+ years of experience in DevOps, Cloud Engineering, or related roles At least 1 year of relevant leadership experience Advanced qualifications in Google Cloud Platform services such as Google Kubernetes Engine (GKE), CloudBuild, Secret Management, and Container Registry, along with hands-on expertise Capability to manage high availability, scalability, and security for Kubernetes clusters and cloud applications Knowledge of Version Control systems like GitHub, including branch strategies and repository management Proficiency in CI/CD tools such as GitHub Actions, Jenkins, or similar platforms Expertise in creating and managing Dockerfiles and Kubernetes YAML configurations Understanding of infrastructure automation tools such as Terraform and configuration management tools like Ansible Skills in troubleshooting Kubernetes clusters, network connectivity issues, and distributed system performance bottlenecks Familiarity with diagnosing and debugging Python-based applications and other cloud-native technologies Background in enterprise AI integration using AWS Bedrock, Google Vertex AI, and Azure AI Services Expertise in designing, building, and operating AI agents and agentic AI frameworks Knowledge of Retrieval-Augmented Generation (RAG) architectures and their implementation Flexibility to use AI-assisted development tools, such as Cursor, Claude Code, Trae, OpenCode, Antigravity, or similar Understanding of modern Generative AI technologies, LLMs, and enterprise AI solution design