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# Gemini Enterprise SME/Developer

- **Company:** PDDN
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Business Analytics Applications, Data Analysis, Microsoft Azure, Business Systems, Meta-Data Management, Software Engineering, Google Cloud, Chatbots, Large Language Models, Multi-Agent Systems, IT Architecture, Generative AI, Virtual Agents, Looker Analytics
- **Published:** September 3, 2026
- **Apply:** https://www.dice.com/job-detail/eed5ca10-8261-43b4-bcf9-36fdd55ddbe0

## About the Role

5+ years in software development, data/analytics, or AI solution delivery.

Hands-on experience with Generative AI, LLMs, conversational AI, and agent frameworks.

Expertise in Google Gemini Enterprise, Vertex AI, Agent Development Kit (ADK).

Experience with Looker / LookML and semantic layer implementation.

Knowledge of RAG, Agentic AI architectures, vector databases, and AI observability.

Strong understanding of AI governance, security, and compliance.

Experience with semantic modeling, metadata management, and analytics platforms.

Proficiency in cloud platforms (Google Cloud Platform preferred; Azure/AWS acceptable).

Strong stakeholder communication and consulting skills.

Experience driving AI adoption and operating model transformation.

## Description

We are seeking a Gemini Enterprise SME/Developer to design, build, and govern enterprise AI solutions using Google Gemini Enterprise. This role combines agent development, semantic layer governance, AI security, and adoption enablement to drive scalable enterprise AI usage across the organization.

Key Responsibilities

Design and develop conversational AI agents and agentic workflows using Gemini Enterprise.

Expose, manage, and govern the Looker Semantic Layer for AI consumption.

Integrate enterprise data sources, APIs, and business systems with AI agents.

Establish AI guardrails, security controls, governance, monitoring, and performance standards.

Define operating models, adoption KPIs, and user enablement strategies.

Provide guidance on enterprise AI architecture and best practices.


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