Gemini SME

Key Responsibilities

• Excellent in providing L1, L2 & L3 support for Gemini Enterprise App related issues.
• Own the full ServiceNow ticket lifecycle including triage, categorization, prioritization, resolution, and closure with no internal L1/L2/L3 handoff beyond this pair.
• Lead the architecture and rollout of Gemini Enterprise App for enterprise search, summarization, assistants, and agent-driven workflows across business functions.
• Design and implement permissions-aware search and retrieval across structured and unstructured enterprise content.
• Configure, manage & support prebuilt connectors for Google and third-party enterprise systems such as Confluence, Jira, SharePoint, ServiceNow, and other knowledge sources.
• Build custom mcp server: Develop & Deploy for non-standard enterprise systems and APIs, including data ingestion, indexing, metadata handling, ACL mapping, and synchronization design.
• Assist, Guide, Create, troubleshoot operationalize no-code / low-code agents using Agent Designer, including single-step and multi-step workflows, orchestration, and recurring task automation.
• Support onboarding and governance of Google-made, third-party, and internally built agents, including custom agents built by internal engineering teams.
• Define and implement identity and access strategies using Google Identity and Workforce Identity Federation, including SSO, group/claim mapping, access-controlled data sources, and user authorization models.
• Partner with IAM and security teams on OIDC/SAML federation, attribute mapping, group-based access, and SCIM-based provisioning where required.
• Establish best practices for prompt design, grounding, response quality, evaluation, and responsible AI adoption across enterprise use cases
• Work with business stakeholders to identify high-value use cases, prioritize rollout waves, and measure adoption, quality, and business impact.
• Create architecture standards, implementation playbooks, and operational runbooks for support and scale.
• Implement, Support GCP Model Armor to protect AI models and agents from adversarial threats, vulnerabilities, and misuse in production environments.
• Establish and refine generative AI evaluation frameworks to assess model performance, accuracy, bias, reliability, and alignment with business KPIs continuously.
• Define and implement comprehensive security strategies: enforce least privileged IAM, monitor agent activities, employ VPC Service Controls, and ensure data encryption and compliance with organizational policies.
• Must be proficient with Terraform, GITHUB Actions & in Python Language.
• Monitor and troubleshoot connectors health, performance, and security incidents using Google Cloud Operations and custom telemetry integrations.
• Document architecture designs, standard operating procedures, troubleshooting guides, and best practices for deploying and managing Gemini Enterprise in production.

Skill Requirements

Required Skills and Expertise

• 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.
• Familiarity with no-code/low-code automation platforms within AI ecosystems to support rapid agent prototyping and deployment.
• 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.
• Excellent documentation, communication, and collaboration skills to work effectively across technical and business stakeholders.

Other Requirements

Qualifications & Certifications:

• Bachelor’s degree in IT/ Engineering/MBA or other management qualification.
• GCP Cloud Architect Certification / GCP Devops / ML Engineer Architect Certification
• Terraform Associate