Google Cloud & AI Solutions Engineer
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Key skills for this role
Role Overview
The role combines pre-sales solution engineering with hands-on post-sales delivery across Google Cloud technologies.
The engineer will lead discovery, architect cloud environments, deploy production workloads, and develop custom AI agents and generative AI solutions.
Key Skills for This Role
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Job Scope
The role combines pre-sales solution engineering with hands-on post-sales delivery across Google Cloud technologies.
The engineer will lead discovery, architect cloud environments, deploy production workloads, and develop custom AI agents and generative AI solutions.
Pre-Sales and Architecture
- Co-lead technical discovery, qualify requirements, author RFP and RFI responses, and draft statements of work.
- Design enterprise architectures, cost estimates, and migration blueprints on Google Cloud.
- Deliver technical demonstrations and executive presentations to technical and C-level stakeholders.
Cloud Delivery and Migration
- Architect, deploy, and automate Google Cloud landing zones covering organization hierarchy, IAM, VPC networking, security perimeters, and billing.
- Build and manage Infrastructure as Code pipelines with Terraform.
- Lead workload migration and application deployment using Compute Engine, GKE, Cloud Run, Cloud SQL, and Spanner.
Applied AI Development
- Build production-ready AI agents using Vertex AI, Gemini models, and agentic orchestration frameworks.
- Develop RAG pipelines, grounding search, and enterprise tool-use or function-calling integrations.
- Create proof-of-concepts for agentic workflows, document processing, and generative AI use cases.
Education and Experience
- Bachelor’s degree in Computer Engineering, Computer Science, Artificial Intelligence, or a related field.
- Four to six years of technical engineering experience across cloud architecture, DevOps, and delivery.
- One to two or more years of hands-on experience with generative AI, RAG pipelines, or agentic workflows.
Position Requirements
- Solution architecture and consultative selling capability.
- Hands-on technical agility and troubleshooting capability.
- End-to-end delivery accountability.
- Ability to translate complex AI and cloud concepts into business value.
- Professional Cloud Architect or Professional Data Engineer certification is required.
- Professional Machine Learning Engineer or Google Cloud Gen AI Leader / Developer credentials are preferred.
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