• Extensive experience designing and architecting solutions on Google Cloud Platform across IaaS, PaaS, and SaaS environments.
• Proven experience defining enterprise-scale migrations to Google Cloud, with a strong understanding of migration strategies including rehost, replatform, refactor, repurchase, retire, retain, and relocate.
• Broad hands-on knowledge of core Google Cloud services across compute, containers, storage, databases, networking, security, observability, DevOps, and cost management.
• In-depth experience designing Google Cloud landing zones and cloud foundations, including resource hierarchy, projects, billing, IAM, shared VPC, hub-and-spoke networking, service perimeters, guardrails, logging, monitoring, governance, and security controls.
• Experience architecting secure, scalable, and highly available cloud environments, including integration between on-premises infrastructure and Google Cloud.
• Strong experience with containerised solutions, Kubernetes production platforms, microservices, and ideally Google Kubernetes Engine.
• Experience deploying infrastructure using automation and Infrastructure as Code, particularly Terraform.
• Experience designing AI-ready cloud and data platforms that support ingestion, storage, governance, analytics, model deployment, monitoring, and integration with enterprise applications.
• Understanding of generative AI solution patterns, such as retrieval-augmented generation, enterprise search, intelligent assistants, document processing, content generation, and AI-enabled workflow automation.
• Awareness of AI governance, responsible AI, data privacy, security, model risk, auditability, and compliance considerations for AI-enabled cloud solutions.
• Strong analytical and technical skills, with the ability to learn new tools, services, and methodologies as the market evolves.
• Ability to identify opportunities to use automation, AI-assisted engineering, AIOps, and cloud-native tooling to improve reliability, observability, delivery speed, and operational efficiency.
• Excellent English verbal and written communication skills, with the ability to engage effectively with both technical and non-technical stakeholders.
• Ability to work well under pressure, respond quickly to client needs, and adapt to changing requirements.

Good to have:

• Pre-sales or commercial experience.
• Previous experience with on-premises infrastructure management.
• Comfortable with Continuous Integration and Continuous Deployment concepts and common approaches using tools such as Jenkins, Git, Azure DevOps, GitHub Actions, GitLab CI/CD, Cloud Build, or Cloud Deploy.
• Experience with Anthos, Apigee, Cloud Composer, Dataflow, Pub/Sub, Looker, Vertex AI, Model Garden, Document AI, Gemini, or other Google Cloud data, analytics, API management, AI, or generative AI services.
• Experience designing retrieval-augmented generation architectures using vector search, embeddings, enterprise data sources, access controls, and secure application integration patterns.
• Experience with MLOps, including model lifecycle management, CI/CD for ML, feature stores, model monitoring, experimentation, and deployment pipelines.
• Experience with AIOps, intelligent observability, automated remediation, anomaly detection, and AI-assisted cloud operations.
• Experience with cloud security, compliance, and governance frameworks, including considerations specific to AI-enabled workloads.
• Google Cloud certifications, such as Professional Cloud Architect, Professional Cloud DevOps Engineer, Professional Cloud Security Engineer, Professional Cloud Network Engineer, Professional Data Engineer, Professional Machine Learning Engineer, or Associate Cloud Engineer.