Design, develop, and deploy AI-powered chatbot solutions on Google Cloud Platform. Build and maintain Generative AI applications. Strong Python development experience required.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Job Description
Python GCP AI Engineer (AI Chatbot Development) - 100% Remote
Location: Brazil (Remote)
Duration: 12+ Months
Rate: DOE
Job Summary
We are seeking a highly skilled Python GCP AI Engineer with proven experience in designing, developing, and deploying AI-powered chatbot solutions on Google Cloud Platform (GCP). This is not a traditional DevOps or Cloud Infrastructure role. The ideal candidate must have hands-on experience working on Generative AI projects using the GCP AI ecosystem, including Gemini models and Vertex AI.
Mandatory Skills
- Strong Python development experience.
- Hands-on experience building AI Chatbots on Google Cloud Platform (GCP).
- Experience with Vertex AI, Gemini Models, and GCP AI services.
- Experience implementing Retrieval-Augmented Generation (RAG) architectures.
- Knowledge of Prompt Engineering, prompt optimization, and prompt evaluation.
- Experience integrating LLMs with enterprise applications using REST APIs.
- Strong understanding of Vector Databases, embeddings, and semantic search.
- Experience implementing AI guardrails, content safety, and responsible AI practices.
- Knowledge of Google Cloud services such as Cloud Run, Cloud Functions, BigQuery, Cloud Storage, Pub/Sub, and IAM.
- Experience monitoring AI applications, token consumption, latency, and cost optimization.
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Roles & Responsibilities
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- Design, develop, and deploy enterprise AI chatbot solutions using the GCP AI technology stack.
- Build and maintain Generative AI applications utilizing Vertex AI and Gemini foundation models.
- Develop scalable backend services in Python for AI-powered applications.
- Implement Retrieval-Augmented Generation (RAG) pipelines using enterprise knowledge sources.
- Optimize prompts, retrieval strategies, and AI guardrails to improve chatbot performance.
- Monitor token usage and drive token cost optimization initiatives.
- Lead incident triage, root cause analysis, and release rollback decisions for AI applications.
- Own end-to-end technical delivery, architecture, deployment, and production support.
- Maintain operational runbooks, knowledge base documentation, and governance standards.
- Conduct weekly service reviews, monthly governance meetings, and risk reporting.
- Collaborate with business stakeholders to continuously improve AI chatbot capabilities.
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