Job Summary

Role Overview

We are looking for a Senior Forward Deployment Engineer (FDE) to lead the technical architecture and end-to-end deployment of enterprise Generative AI and Agentic AI solutions for strategic customers on Google Cloud. The Senior FDE is a customer-facing solution architect who can code: they own the technical success of the customer engagement, make architecture and deployment decisions, lead customer workshops, review implementation, drive production readiness, and mentor FDEs.

• Customer-facing solution architect who can code
• Owns technical success of the customer engagement
• Makes architecture and production decisions
• Leads FDEs and influences the platform roadmap

Typical time allocation

• ~50% architecture and technical leadership
• ~30% customer leadership
• ~20% engineering oversight and mentoring

Key Responsibilities

• Own the overall technical delivery of enterprise Agentic AI deployments from discovery and architecture through production rollout, hypercare, and transition to BAU.
• Lead customer discovery, technical workshops, architecture reviews, executive technical discussions, solution demonstrations, POCs, pilots, and production planning.
• Design customer-specific AI and cloud architectures using Gemini Enterprise Agent Platform, ADK, Google Cloud services, enterprise data sources, applications, security controls, and operational services.
• Define agent architecture including multi-agent patterns, Planner/Critic/Supervisor/Orchestrator designs, domain agents, Skills, tools, RAG, human-in-the-loop controls, state, and enterprise integrations.
• Define the integration architecture for REST APIs, MCP tools, databases, ITSM platforms, monitoring systems, identity systems, messaging systems, and proprietary applications.
• Decide which requirements should be implemented through configuration, customer-specific extensions, or reusable platform capabilities; drive the appropriate engineering path.
• Define production deployment architecture across Agent Runtime, Cloud Run, GKE, Cloud Functions, Pub/Sub, Cloud Storage, IAM, Secret Manager, networking, and other appropriate Google Cloud services.
• Define production architecture requirements for availability, scalability, security, resiliency, observability, release management, rollback, disaster recovery, and operational support.
• Lead security and identity architecture discussions covering IAM, RBAC, service accounts, authentication, authorization, secrets management, data protection, auditability, and network controls.
• Define and review production readiness criteria covering functionality, security, performance, reliability, evaluation, observability, governance, supportability, and operational readiness.
• Define the customer AgentOps strategy including telemetry, tracing, logging, metrics, evaluation, quality monitoring, cost monitoring, and operational dashboards.
• Lead complex troubleshooting and root-cause analysis for production issues involving agents, models, RAG, tools, integrations, networking, authentication, and cloud infrastructure.
• Drive reliability, latency, scalability, model performance, and AI cost optimization across customer deployments.
• Define and review CI/CD, release management, environment promotion, versioning, rollback, and deployment processes.
• Review architecture, integration code, deployment plans, and technical deliverables produced by FDEs; establish engineering quality standards.
• Mentor and technically guide FDEs and Associate FDEs working on customer deployments.
• Establish reusable deployment patterns, integration patterns, reference architectures, runbooks, onboarding standards, and troubleshooting playbooks.
• Identify recurring customer requirements and drive their conversion into reusable agents, Skills, tools, connectors, frameworks, and platform capabilities with Agent Development / Platform Engineering teams.
• Act as the senior technical escalation point for complex customer issues and major production incidents.
• Provide technical feedback to Product, Agent Development, Platform Engineering, Security, Cloud Architecture, and Operations teams and influence platform roadmap priorities.
• Lead technical handover, customer enablement, operational readiness, and transition to support/BAU teams.

Skill Requirements

Must Have Skills

• Strong Python and software engineering skills with the ability to review and guide production code.
• Extensive hands-on experience with Generative AI, LLMs, RAG, prompt engineering, tool calling, evaluation, and multi-agent architectures.
• Strong hands-on experience with ADK and/or LangGraph/LangChain in production.
• Strong Google Cloud architecture and engineering experience with Gemini Enterprise Agent Platform / Google Cloud AI services.
• Strong enterprise integration experience across REST APIs, databases, ITSM platforms, monitoring systems, identity platforms, and customer-specific applications.
• Strong understanding of IAM, OAuth, service accounts, RBAC, secrets management, authentication, authorization, security, and enterprise networking.
• Strong experience with containerized applications, Docker, Cloud Run, GKE, and production deployment patterns.
• Strong experience with CI/CD, Git, release management, versioning, and production operations.
• Strong experience with observability, tracing, logging, monitoring, debugging, evaluation, and production incident management.
• Strong understanding of RAG architecture, knowledge integration, retrieval, evaluation, and agent quality measurement.
• Experience with Responsible AI, guardrails, AI security, data protection, and enterprise governance.
• Strong customer-facing communication skills and ability to lead architecture conversations with senior technical stakeholders.

Preferred Skills

• Deep experience with Gemini Enterprise Agent Platform capabilities including ADK, Agent Runtime, Agent Gateway, Model Armor, Agent Evaluation, and Cloud Observability.
• Experience with OpenTelemetry and enterprise AgentOps practices.
• Experience with MCP, A2A, and enterprise agent interoperability.
• Experience with Terraform / Infrastructure-as-Code and enterprise cloud landing zones.
• Strong experience with GKE, Cloud Run, Pub/Sub, BigQuery, Cloud Storage, Secret Manager, networking, and private connectivity.
• Experience with ServiceNow, ITSM, CloudOps, SRE, AIOps, infrastructure automation, or enterprise operations.
• Experience designing highly available, scalable, secure production architectures.
• Experience leading POCs, pilots, MVPs, production rollouts, and strategic enterprise deployments.
• Exposure to multiple cloud platforms is advantageous.

Other Requirements

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or related field.
• 8–12 years of overall software engineering, cloud engineering, solution architecture, or equivalent technical experience.
• At least 3 years of practical Generative AI / Agentic AI experience.
• Demonstrated track record of delivering multiple enterprise technology or AI solutions into production.
• Demonstrated customer-facing technical leadership and architecture ownership.

Key Attributes

• Strong customer-facing presence and ability to build technical trust with enterprise customers.
• Strong ownership of outcomes rather than individual tasks.
• Excellent architecture and problem-solving skills.
• Comfortable operating across AI engineering, cloud architecture, integration, security, and operations.
• Able to make pragmatic decisions between reusable platform capabilities and customer-specific implementation.
• Strong mentoring and technical leadership capability.