Forward Deployed Engineer, GenAI, Google Cloud (English, Korean)
Forward Deployed Engineer, GenAI, Google Cloud (English, Korean)
Google India5-7 Years
- Posted 25 days ago
- Be among the first 10 applicants
Job Description
Google will be prioritizing applicants who have a current right to work in Singapore, and do not require Google's sponsorship of a visa.Minimum qualifications:
Responsibilities
- 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 architecting AI systems on cloud platforms (e.g., GCP).
- Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
- Experience taking production-grade AI-driven solutions from conception to launch for customers and leading technical discovery sessions with customers.
- Ability to communicate in Korean and English fluently to conduct necessary business interaction in Korean and English.
- 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, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
- Knowledge of LLM-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
Responsibilities
- Serve as a developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, 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 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 the Engineering teams.
- Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.



