BENGALURU · FULLTIME
Generative AI Forward Deployed Engineer, Google Cloud
Bengaluru · onsite · Posted 12d ago
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Not disclosed8–10 yrsfulltimeBengaluru
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Section · 01
About this role
In most instances, this position requires in-person interviews as part of the hiring process.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mumbai, Maharashtra, India; Pune, Maharashtra, India; Bengaluru, Karnataka, India.Minimum qualifications:
Responsibilities
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mumbai, Maharashtra, India; Pune, Maharashtra, India; Bengaluru, Karnataka, 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 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 and architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
- Experience leading technical discovery sessions.
- Master's 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, 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.
- 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.
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Section · 02
Skills
CrewaiCloud Computingvector databasesAI-driven SolutionsMulti-Agent SystemsADKReact.Jsgranular tracingLLM-native metricsRAG-like architecturesState ManagementLanggraph
Section · Company
About Google
Software Product
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