Overview
Skills
Job Details
GEN AI Architect and Prompt Engineer
• Technical Proficiency:
o Google Cloud Platform: Extensive, hands-on experience with Google Cloud Platform, specifically Vertex AI, Dialogflow CX, and other conversational AI services.
o Programming: Proficiency in programming languages like Python is essential for building integrations, automating tasks, and analyzing data.
o APIs & Data: Experience with REST APIs, webhooks, and data management systems.
• Conversational Expertise:
o Linguistics: A strong understanding of human language, syntax, and conversational nuances.
o Prompt Engineering: Proven experience in crafting effective prompts for Large Language Models (LLMs) and generative AI.
o Conversation Building: Experience in designing and building engaging and logical conversations.
Responsibilities:
AI Architecture & Development / Support
• Design and Implement AI Solutions: Architect, design, and deploy end-to-end conversational AI agents and virtual assistants on the Google Cloud Platform platform.
• Platform Expertise: Utilize a deep knowledge of Google Cloud Platform AI technologies, including Vertex AI, Dialogflow CX, Conversational AI (CCAI), Playbooks, Conversational Agents and related services.
• Integrations: Build and manage integrations between conversational agents and various backend systems, APIs, and other Google Cloud services.
• Lifecycle Management: Manage the full lifecycle of AI agents, from initial proof-of-concept to production deployment and ongoing maintenance. This includes monitoring performance, troubleshooting issues, and implementing MLOps best practices.
Prompt Engineering & Conversation Building
• Prompt Design: Craft and refine prompts and instructions for generative AI models to ensure accurate, relevant, and helpful responses.
• Conversation Design: Design natural and effective conversational flows for both voice and chat agents. This involves defining user journeys, intents, entities, and state handlers.
• Agent Tuning: Tune and optimize NLU (Natural Language Understanding) models and AI agent performance through data analysis and iterative refinement.
• Content & Ethics: Ensure that AI-generated content is high-quality, on-brand, and adheres to responsible AI principles, including safety and fairness.
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