Gemini Enterprise

Dear Candidate,

Kindly find below Job description

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Location - Chennai, Noida, Bangalore, Pune, Hyderabad

Gemini Enterprise SME

Position Overview

We are looking for a hands-on Lead Engineer / Solution Architect to design, implement, and scale Google Gemini Enterprise across the enterprise. This role will own enterprise search and summarization, low-code/no-code agent creation, connector onboarding, identity and access integration, custom connector development, and AI security guardrails. The person will work closely with business, security, IAM, data, and application teams to deliver secure, permissions-aware, production-ready AI solutions.

Qualifications & Certifications:
• Bachelor’s degree in IT/ Engineering/MBA or other management qualification.
• GCP Cloud Architect Certification / GCP Devops / ML Engineer Architect Certification
• Kubernetes Certification [CKA CKAD etc.]
• Terraform Associate

GCP Services-Focused Responsibilities
• Lead the architecture and rollout of Gemini Enterprise 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 and manage prebuilt connectors for Google and third-party enterprise systems such as Confluence, Jira, SharePoint, ServiceNow, and other knowledge sources.
• Build custom connectors for non-standard enterprise systems and APIs, including data ingestion, indexing, metadata handling, ACL mapping, and synchronization design.
• Create and 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 GCP Model Armor to protect AI models and agents from adversarial threats, vulnerabilities, and misuse in production environments.
• Develop custom Agents using ADK, crew AI, Langraph etc. Framework and deploy on runtime like Cloud Run, GKE, Agent Engine.
• Establish and refine generative AI evaluation frameworks to assess model performance, accuracy, bias, reliability, and alignment with business KPIs continuously.
• Build and maintain foundational GCP infrastructure services supporting Gemini Enterprise, including Compute Engine, GKE (Kubernetes Engine), Cloud Functions, Cloud Storage, Pub/Sub, and BigQuery for data processing and event-driven architectures.
• 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 & in Python Language.
• Monitor and troubleshoot agent platform 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.

Required Skills and Expertise
• 8+ years of experience in cloud architecture, enterprise integration, platform engineering, or solution architecture.
• 4 years 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.