GOOGLE ASIA PACIFIC PTE. LTD.
Forward Deployed Engineer, Delta, Google Cloud (Korean) - Singapore
Visa Source Listed
📍🇸🇬 Singapore📅10h ago
S$1,38,000—S$2,76,000/ year
≈ ₹86L — ₹1.73Cr per year
Azure AIMulti-Agent SystemsReturn on InvestmentGenerative AI Application Development and DeploymentMedical DegreeComputer EngineeringData PipelineTechnical TranslationSafetyComputer ScienceAI EvaluationROICustomer DevelopmentAPIInfrastructure Services
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Job Description
Product area
It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours.
Job description
As a Generative AI (GenAI) Forward Deployed Engineer (FDE) at Google Cloud, you are an embedded builder who bridges the gap between frontier Artificial Intelligence (AI) products and production-grade reality within customers. You will function as an innovator-builder, moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment. You will address blockers to production including solving the integration complexities, data readiness issues, and state-management challenges that prevent AI from reaching enterprise-grade maturity. You will serve dual purpose, providing white glove deployment of complex AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.
Qualifications
Job responsibilities
- Develop for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, Model Context Protocol (MCP) servers) that drive measurable Return on Investment (ROI).
- Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including Application Programming Interface (APIs), legacy data silos, and security perimeters as part of an expert team.
- Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
- Identify repeatable field patterns and friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for engineering teams, and co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.
Minimum qualifications
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- 5 years of experience with software development using Python or similar coding languages.
- Experience building pipelines for structured and unstructured data using both vector databases and Retrieval-Augmented Generation (RAG)-like architectures to power enterprise AI solutions.
- Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
- Experience taking production-grade Artificial Intelligence (AI) solutions from conception to launch for customers.
- Ability to communicate in Korean fluently in order to translate technical concepts to non-technical and executive Korean-speaking audiences.
Preferred qualifications
- Master’s degree or PhD in AI, Computer Science, or a related technical field.
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, Agent Development Kit (ADK)) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
- Experience leading technical discovery sessions.
- Knowledge of Large Language Models (LLM)-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.