GenAI Forward-Deployed Engineering Lead

Google; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Google Cloud is seeking a Forward Deployed Engineering Manager to lead AI/ML engineers who deploy bespoke agentic solutions inside customer environments. You will code, debug, and deploy alongside clients, bridging frontier AI with production readiness.

In this role you’ll mentor engineers, align with Product and GTM leadership, and remove blockers such as data readiness, integration, and state-management challenges, empowering enterprise-scale AI adoption.

Qualifications

• Bachelor's degree in Engineering, CS, or related field, or equivalent practical experience.
• 8 years of experience in cloud computing or a technical customer-facing role.
• 2 years of experience managing a software engineering or similar technical customer-facing team in a cloud computing environment.
• Experience in Python or similar coding languages.
• Experience developing AI/GenAI solutions utilizing AI tools, or designing multi-agent workflows or RAG systems.

Responsibilities

• Lead AI/ML engineers bridging frontier AI products with production-grade solutions at customer sites.
• Provide deep-dive technical mentorship while aligning with Product, Engineering and GTM leadership.
• Resolve data readiness, integration and state-management obstacles hindering enterprise-grade AI.
• Drive customer success and define the cloud-era strategy with the Go-To-Market team.

Skills

Cloud computing

Technical customer-facing

Python

AI/GenAI solutions

Multi-agent workflows

Education

Bachelor’s degree in Engineering, Computer Science, or related field

Tools

Python

Tool calling protocols

AI tools

Job description

Google Cloud is seeking a Forward Deployed Engineering Manager to lead AI/ML engineers who deploy bespoke agentic solutions inside customer environments. You will code, debug, and deploy alongside clients, bridging frontier AI with production readiness.

In this role you’ll mentor engineers, align with Product and GTM leadership, and remove blockers such as data readiness, integration, and state-management challenges, empowering enterprise-scale AI adoption.