Agentic AI Engineer

Capgemini

. Design, develop, and deploy enterprise AI agents and multi-agent solutions using Google's AI ecosystem .

Posted 9/15/2026full-timeRemote • ColombiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and deploying enterprise AI agents and multi-agent solutions using Google's AI ecosystem, with a strong focus on Responsible AI practices, governance, and compliance. Proficient in leveraging cloud services and enterprise data to optimize performance and user experience.

Highest-signal resume keywords

GeminiVertex AIPythonAPI IntegrationResponsible AI

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLAI Agent DevelopmentMulti-Agent SystemsRAG ArchitectureVector DatabasesSemantic SearchBigQueryCloud FunctionsCloud Run

Tools & Technologies

Google Cloud PlatformLookerAgent BuilderLangChainLangGraph

Industry Keywords

Responsible AIAI GovernanceData Warehouse ArchitectureAI Orchestration FrameworksSemantic Models

Tech Stack

Tools & technologies

BigQueryCloudGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Design, develop, and deploy enterprise AI agents and multi-agent solutions using Google's AI ecosystem
Leverage enterprise data, semantic models, and cloud services to deliver scalable, secure, and Responsible AI solutions
Develop and deploy multi-agent solutions using Gemini and Agent Builder
Design and implement RAG solutions leveraging EDW data and Looker semantic models
Integrate AI agents with enterprise APIs, databases, and Google Cloud Platform services
Optimize agent performance, orchestration, and user experience for business-critical use cases
Implement Responsible AI practices, including governance, security, monitoring, and compliance controls

Requirements

What you’ll need

Hands-on experience with Gemini, Vertex AI, and Agent Builder
Strong proficiency in Python, SQL, and API integration
Experience building AI Agents, Multi-Agent Systems, and RAG architectures
Knowledge of vector databases, embeddings, and semantic search
Experience with BigQuery, Cloud Run, Cloud Functions, and other GCP services
Familiarity with Looker, semantic layers, and enterprise data warehouse architectures
Experience with AI orchestration frameworks such as LangChain, LangGraph, or Google ADK
Understanding of Responsible AI, AI governance, security, and model evaluation

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account Capgemini Website LinkedIn All Job Openings 10,000+ employees Founded 1967 💼 Consulting 🏥 Healthcare 📦 Logistics Consulting
Healthcare
Logistics Capgemini is a global leader in partnering with businesses to transform and manage their operations by harnessing the power of technology. With expertise across a wide array of industries such as aerospace, automotive, banking, and healthcare, Capgemini provides a constantly evolving portfolio of services to meet the ever-changing needs of their clients. Their offerings include cloud, cybersecurity, data and artificial intelligence, and enterprise management, among others. Capgemini also emphasizes innovation and sustainability, helping companies achieve digital transformation while promoting environmental and social responsibility. Additionally, Capgemini provides career opportunities across various levels and professions, encouraging innovation and diversity in its workforce. Agentic AI Engineer 🔥 0 minutes ago 🇨🇴 Colombia – Remote ⏰ Full Time 🟡 Mid-level 🟠 Senior 🤖 AI Engineer 👻 Ghost score 12% BigQuery Cloud Google Cloud Platform Python SQL Apply Now Customize resume + cover letter Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
Design, develop, and deploy enterprise AI agents and multi-agent solutions using Google's AI ecosystem
Leverage enterprise data, semantic models, and cloud services to deliver scalable, secure, and Responsible AI solutions
Develop and deploy multi-agent solutions using Gemini and Agent Builder
Design and implement RAG solutions leveraging EDW data and Looker semantic models
Integrate AI agents with enterprise APIs, databases, and Google Cloud Platform services
Optimize agent performance, orchestration, and user experience for business-critical use cases
Implement Responsible AI practices, including governance, security, monitoring, and compliance controls 🎯 Requirements
Hands-on experience with Gemini, Vertex AI, and Agent Builder
Strong proficiency in Python, SQL, and API integration
Experience building AI Agents, Multi-Agent Systems, and RAG architectures
Knowledge of vector databases, embeddings, and semantic search
Experience with BigQuery, Cloud Run, Cloud Functions, and other GCP services
Familiarity with Looker, semantic layers, and enterprise data warehouse architectures
Experience with AI orchestration frameworks such as LangChain, LangGraph, or Google ADK
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