MUMBAI · FULLTIME
Staff Forward Deployed Engineer, Developer AI, Google Cloud
Mumbai · onsite · Posted 47d ago
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Not disclosed8–10 yrsfulltimeMumbai
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Section · 01
About this role
For Singapore applicants:
Google will be prioritizing applicants who have a current right to work in Singapore, and do not require Google's sponsorship of a visa.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Singapore; Bengaluru, Karnataka, India; Gurugram, Haryana, India; Mumbai, Maharashtra, India.Minimum qualifications:
Responsibilities
Google will be prioritizing applicants who have a current right to work in Singapore, and do not require Google's sponsorship of a visa.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Singapore; Bengaluru, Karnataka, India; Gurugram, Haryana, India; Mumbai, Maharashtra, India.Minimum qualifications:
- Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- 8 years of experience in cloud computing or a technical customer-facing role.
- Experience deploying, scaling, and debugging Large Language Model (LLM) or agent-based systems in production environments (including tools, memory, orchestration, evaluation, tracing, and cost/latency profiling).
- Experience with end-to-end technical ownership of engineering projects with executive stakeholders.
- 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 with agentic frameworks and harness layers, such as Google's Agent Development Kit (ADK) or equivalent, protocol-level interoperability (MCP, Agent-to-Agent (A2A)) across third-party Independent Software Vendor (ISV) platforms (e.g., ServiceNow), and security ecosystem in DevSecOps.
- 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, 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 APIs, legacy data silos, and security perimeters as part of an expert team.
- Design and deploy production-grade agentic developer workflows on Google Cloud's AI stack, executing large-scale refactors, language migrations, Specification-to-Pull Request pipelines, and automated review/incident-to-fix loops.
- Embed with customer's staff engineers and leaders to identify core SDLC bottlenecks, such as legacy migrations, test coverage, review latency, or onboarding friction, and define success metrics.
- Integrate Google's agentic systems into the customer's existing ISV and tools (e.g., Teamwork Graph, GitLab, ServiceNow, Slack) leveraging MCP and A2A protocols.
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Section · 02
Skills
automated review incident-to-fix loopscost latency profilingevaluation tracingprotocol-level interoperabilitySpecification-to-Pull Request pipelinesCrewaiapisDevsecopsMulti-Agent Systemsagent-based systemsmemory orchestrationcost-per-requestgranular tracingLLM-native metricsState ManagementLanggraph
Section · Company
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