Gemini Enterprise Engineer

Overview

We are seeking a skilled Gemini Enterprise Engineer to design, implement, integrate, and support enterprise-grade generative AI solutions using Google Cloud technologies. The ideal candidate has experience with Google Cloud Platform, enterprise search, generative AI, data integration, and AI agent development. This role works closely with business stakeholders, cloud engineers, security teams, and application developers to transform business processes into secure, scalable, AI-powered workflows.

Responsibilities

Key ResponsibilitiesGemini Enterprise Implementation

• Design, configure, and deploy Gemini Enterprise applications based on customer and business requirements.
• Build AI-powered enterprise search, knowledge discovery, question-answering, and workflow automation solutions.
• Configure applications, data stores, search experiences, actions, and AI agents.
• Develop reusable AI agents that support operations, customer service, sales, human resources, and knowledge management.Data Integration and Enterprise Search
• Connect Gemini Enterprise with structured and unstructured enterprise data sources.
• Integrate data from Google Workspace, Microsoft 365, databases, document repositories, cloud storage, APIs, and third-party business applications.
• Design secure data ingestion, indexing, synchronization, and retrieval processes.
• Improve search quality and generated responses through metadata configuration, document preparation, access controls, grounding, and retrieval optimization.
• Ensure applications are connected to the appropriate data stores to support search results, answers, actions, and agents.Security and Governance
• Implement identity, access management, data permissions, and role-based access controls.
• Ensure Gemini Enterprise solutions comply with organizational security, privacy, and governance policies.
• Configure secure access to enterprise information based on existing user permissions.
• Work with security and compliance teams on data residency, auditability, retention, and responsible AI requirements.
• Identify and mitigate risks such as data leakage, prompt injection, unauthorized access, hallucinations, and inappropriate AI responses.
• Maintain documentation covering system architecture, data flows, access controls, and operational procedures.Deployment and Operations
• Support Gemini Enterprise solutions throughout development, testing, production deployment, and ongoing operations.
• Monitor application usage, search quality, agent performance, errors, latency, and user feedback.
• Troubleshoot data connector, indexing, authentication, API, permission, and application issues.
• Implement logging, monitoring, alerting, testing, and operational support processes.
• Recommend improvements based on adoption metrics, evaluation results, and stakeholder feedback.
• Stay current with Gemini Enterprise, Gemini models, Vertex AI, and related Google Cloud services.Stakeholder Collaboration
• Conduct discovery sessions to understand business requirements, workflows, data sources, and expected outcomes.
• Translate business requirements into technical architecture and implementation plans.
• Create proofs of concept, demonstrations, solution documentation, and technical presentations.
• Collaborate with project managers, architects, developers, data engineers, security teams, and business users.
• Train administrators, developers, support teams, and end users on Gemini Enterprisecapabilities and responsible usage.
• Communicate technical risks, dependencies, progress, and recommendations to stakeholders.Required Qualifications
• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field, or equivalent professional experience.
• At least 3 years of experience in cloud engineering, software development, data engineering, AI engineering, or enterprise application implementation.
• Hands-on experience with Google Cloud Platform.
• Experience with generative AI, large language models, conversational AI, or enterprise search technologies.
• Experience integrating applications with APIs, databases, cloud storage, and third-party systems.
• Proficiency in at least one programming language, preferably Python, Java,JavaScript/TypeScript, or Go.
• Understanding of REST APIs, authentication, OAuth, service accounts, and identity and access management.
• Knowledge of prompt engineering, retrieval-augmented generation, grounding, embeddings, and semantic search.
• Familiarity with structured and unstructured data ingestion.
• Experience with application testing, troubleshooting, logging, and monitoring.
• Strong analytical, documentation, communication, and stakeholder-management skills.

Qualifications

Preferred Qualifications

• Hands-on experience implementing Gemini Enterprise or related Google Cloud AI products.
• Experience with Vertex AI, Gemini APIs, Vertex AI Search, Agent Builder, or conversational AIplatforms.
• Experience developing enterprise AI agents and multi-step agentic workflows.
• Experience integrating Google Workspace or Microsoft 365 data.
• Knowledge of BigQuery, Cloud Storage, Cloud Run, Cloud Functions, Pub/Sub, and API management.
• Experience with document processing, vector search, embeddings, or knowledge-management platforms.
• Familiarity with infrastructure as code and CI/CD tools such as Terraform, GitHub Actions, or Cloud Build.
• Understanding of responsible AI, model evaluation, AI governance, and enterprise data privacy.
• Experience in client-facing consulting, professional services, or enterprise implementation environments.
• Relevant Google Cloud certifications, such as Professional Machine Learning Engineer,Professional Cloud Architect, Professional Data Engineer, Professional Cloud Developer, or Generative AI Leader. Key Technical Skills
• Gemini Enterprise and Gemini APIs
• Vertex AI and enterprise search
• AI agents and agentic workflows
• Retrieval-augmented generation and prompt engineering
• Python, Java, JavaScript/TypeScript, or Go
• REST APIs and systems integration
• Google Workspace and Microsoft 365 integrations
• Identity and Access Management
• BigQuery, Cloud Storage, and data ingestion
• Logging, monitoring, evaluation, responsible AI, and AI governanceCore Competencies
• Strong problem-solving and analytical abilities.
• Ability to translate business needs into practical AI solutions.
• Strong attention to data security, privacy, and governance.
• Clear technical and business communication.
• Ability to manage multiple implementation priorities.
• Collaborative and customer-focused approach.
• Ability to work independently while coordinating with cross-functional teams.