Google Cloud and AI Solutions Engineer

Job Title

Google Cloud and AI Engineer

Job Scope

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

 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.

 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).

 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

• Solution Architecture & Consultative Selling

• Hands-on Technical Agility & Troubleshooting

• End-to-End Delivery Accountability

• Translating Complex AI/Cloud Concepts to Business Value

 Required: Google Cloud Certified Professional Cloud Architect or Professional Data Engineer.

 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.