Google Cloud & AI Solutions Engineer

About the role

The Cloud & AI Solutions Engineer will drive both pre‑sales solution engineering and hands‑on post‑sales delivery across the full spectrum of Google Cloud technologies. You will work closely with sales teams to shape customer requirements and then design, build, and migrate enterprise‑grade workloads while creating custom generative AI agents.

Key responsibilities

• Co‑lead technical discovery, qualify client needs, respond to RFP/RFI and draft statements of work.
• Design target‑state enterprise architectures, cost estimates and migration blueprints on GCP.
• Deliver technical demos and executive presentations to technical leads and C‑level stakeholders.
• Architect, deploy and automate Google Cloud Landing Zones (IAM, VPC, security perimeters, billing).
• Build and manage Infrastructure‑as‑Code pipelines using Terraform.
• Lead end‑to‑end workload migration and modern application deployment (Compute Engine, GKE, Cloud Run, Cloud SQL, Spanner).
• Design, build and deploy production‑ready AI agents using Vertex AI, Gemini models and orchestration frameworks such as LangChain, LlamaIndex or Google GenAI SDK.
• Create Retrieval‑Augmented Generation pipelines, grounding search and enterprise tool‑use integrations.
• Develop functional PoCs demonstrating agentic workflows, document processing and generative AI use cases.

Required profile

• Bachelor’s degree in Computer Engineering, Computer Science, Artificial Intelligence or a related field.
• 4–6 years of technical engineering experience covering cloud architecture, DevOps and delivery (both pre‑sales and post‑sales).
• 1–2+ years of hands‑on experience building applied generative AI solutions, RAG pipelines or agentic workflows.
• Google Cloud Certified Professional Cloud Architect or Professional Data Engineer (required).
• Preferred: Google Cloud Professional Machine Learning Engineer or Google Cloud Gen AI Leader/Developer credentials.

Required skills

• Google Cloud Platform (GCP) services: IAM, VPC, Landing Zones, Compute Engine, GKE, Cloud Run, Cloud SQL, Spanner.
• Infrastructure‑as‑Code with Terraform.
• DevOps automation and CI/CD pipelines.
• Vertex AI, Gemini models, Google GenAI SDK.
• Agentic frameworks: LangChain, LlamaIndex.
• Retrieval‑Augmented Generation (RAG) pipeline development.