About the Role
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The Cloud & AI Solutions Engineer is responsible for driving both pre-sales solution engineering and hands-on post-sales delivery across the full spectrum of Google Cloud technologies. This includes leading technical discovery, architecting enterprise Google Cloud landing zones, deploying production-grade cloud workloads, and designing and developing custom AI agents and generative AI solutions to drive customer modernization.
Main Duties and Responsibilities
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Pre-Sales & Solution Architecture:
- Co-lead technical discovery with the sales team, qualify client requirements, author RFP/RFI responses, and draft statements of work (SOWs).
- Design target-state enterprise architectures, cost estimations, and architectural migration blueprints on GCP.
- Deliver high-impact technical demonstrations and executive presentations to technical leads and C-level stakeholders.
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Cloud Infrastructure Delivery & Migration (Post-Sales):
- Architect, deploy, and automate enterprise-grade Google Cloud Landing Zones (organization hierarchy, IAM, VPC networking, security perimeters, and billing models).
- Build and manage Infrastructure as Code (IaC) pipelines using Terraform.
- Lead end-to-end workload migration and modern application deployment (compute engines, Google Kubernetes Engine / GKE, Cloud Run, Cloud SQL, Spanner).
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Applied AI & Agentic Development:
- Design, build, and deploy production-ready AI Agents leveraging Vertex AI, Gemini models, and agentic orchestration frameworks (e.g., LangChain, LlamaIndex, or Google GenAI SDK).
- Build Retrieval-Augmented Generation (RAG) pipelines, grounding search, and enterprise tool-use/function-calling integrations.
- Develop functional Proof of Concepts (PoCs) demonstrating agentic workflows, document processing, and generative AI use cases during pre-sales and post-sales delivery.
Position Requirements
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Solution Architecture & Consultative Selling
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Hands-on Technical Agility & Troubleshooting
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End-to-End Delivery Accountability
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Translating Complex AI/Cloud Concepts to Business Value
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Required: Google Cloud Certified Professional Cloud Architect or Professional Data Engineer.
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Preferred: Google Cloud Professional Machine Learning Engineer or Google Cloud Gen AI Leader / Developer credentials.
Education
Bachelor’s degree in Computer Engineering, Computer Science, Artificial Intelligence, or any other related field.
Experience
- 4 to 6 years of technical engineering experience spanning cloud architecture, DevOps, and delivery (combining pre-sales and post-sales).
- 1 to 2+ years of hands-on experience building applied generative AI solutions, RAG pipelines, or agentic workflows.
