Why This Role Stands Out
This hybrid role offers a fantastic opportunity to lead the architecture and rollout of cutting-edge Gemini Enterprise App solutions, leveraging your Google Cloud expertise. You'll thrive here if you possess strong problem-solving skills and a passion for driving innovation in enterprise search and workflow automation. Apply now to make a significant impact within a reputable company.
Quick Overview
Job Description
Key Responsibilities: Gemini Enterprise App SME We are looking for a hands-on Lead Engineer / Solution SME to support, implement, and scale Google Gemini Enterprise App across the enterprise. This role will own supporting L1,L2 & L3 issues related enterprise search and summarization, low-code/no-code agents, connectors onboarding, identity and access, custom mcp server, integration & Deployment, and AI security guardrails.
The person will work closely with business, security, IAM, data, and application teams to deliver secure, permissions-aware, production-ready Gemini Enterprise App • 8+ years of experience in cloud architecture, enterprise integration, platform engineering, or solution architecture. • ITSM fundamentals: incident vs. service request, prioritization, SLA/OLA concepts, ticket hygiene. • ServiceNow proficiency: queue management, routing, and knowledge-base use. • Vendor and stakeholder management (Google Cloud Customer Care/TAM, customer executives, HCLTech delivery leadership). • Strong experience with Google Cloud Platform services, especially IAM, APIs, security, and application integration. • 2+ years of hands-on experience in GenAI / LLM / RAG / enterprise search implementations. • Strong understanding of identity management integration strategies for cloud-native applications, including Google Workspace, OAuth, and SAML. • Expertise in deploying and managing AI security tools, especially Google’s Model Armor for protecting generative AI models. • Experience configuring third-party API connectors and integrating diverse SaaS or on-premises applications with AI agents. • Proficiency in AI/ML evaluation methodologies testing model fairness, accuracy, robustness, and continuous monitoring. • Strong infrastructure-as-code (Terraform) and CI/CD skills for automated, repeatable deployments. • Knowledge of cloud security best practices, network segmentation, IAM policies, and compliance frameworks relevant to AI and data-intensive systems. • Experience with monitoring tools like Cloud Monitoring, Cloud Logging, Prometheus, and custom telemetry to ensure high availability and security posture.
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