The client is seeking an experienced AI Developer to design, develop, deploy, and support enterprise-grade Agentic AI solutions on the Google Gemini Enterprise Agent Platform (GEAP). In this role, you will focus on building scalable AI agents, enterprise integrations, and production-ready AI services while collaborating closely with architects, platform engineers, and business stakeholders to deliver secure, reliable, and cost-effective solutions.
What will your role look like
• Design and develop scalable AI agents, workflows, and enterprise integrations using Python.
• Build seamless platform integrations using APIGEE, REST APIs, and Model Context Protocol (MCP).
• Develop cloud-native solutions on GCP leveraging Gemini models, GEAP, and Google AI services.
• Implement key AI patterns including prompt engineering, grounding, context management, RAG, and memory frameworks.
• Evaluate, test, deploy, monitor, and continuously optimize AI agent performance.
• Analyze token consumption, model usage, scalability, and operational costs to maintain efficient delivery.
Why you will love this role
• Work on cutting-edge Agentic AI solutions and multi-agent systems using industry-leading platform tools.
• Collaborate with cross-functional teams of architects, platform engineers, and key business stakeholders.
• Drive impact by delivering enterprise-ready, production-grade AI technologies at scale.
• Thrive in a collaborative, innovative culture focused on technical excellence and career growth.
We would like you to bring along
• 5+ years of strong Python development experience for enterprise applications and AI solutions.
• Hands-on experience with Agentic AI, multi-agent architecture, and orchestration patterns.
• Solid understanding of GCP, Gemini models, GEAP, Vertex AI, or Google ADK.
• Demonstrated experience with APIGEE, RESTful APIs, integrations, and MCP-based connectivity.
• Strong working knowledge of model selection, prompt engineering, grounding, agent evaluation, and cost monitoring.
• Familiarity with software engineering best practices including Git, CI/CD pipelines, testing, debugging, and secure development principles.