Harnessing the Power of Knowledge: Seeking Experienced AI Leader

Há 2 dias

Buenos Aires, Brasil beBeeArchitecture Tempo inteiro R$120.000 - R$150.000
Senior AI Engineer - RAG Architect

Unlock community knowledge in a new way. As a Senior AI Engineer, you will lead the design and implementation of an end-to-end Retrieval-Augmented Generation (RAG) architecture to power autoscripting, onboarding search, and long-term knowledge reuse.

The ideal candidate is an experienced leader who can oversee multiple aspects of this complex system, including ingestion and retrieval layers, CI orchestrations, model and prompt governance, UI onboarding tool architecture, data security, and compliance.

This role requires a strong technical background in LangChain, ChromaDB, Qdrant, or pgvector, as well as cross-encoder rerankers. The candidate should also have experience with Python (FastAPI or Flask) and ability to analyze Java codebases for chunking boundaries.

Key Responsibilities:
  • Design and Implement RAG Architecture: Lead the development of an end-to-end RAG system, including ingestion and retrieval layers, to power autoscripting, onboarding search, and long-term knowledge reuse.
  • Oversee CI Orchestrations: Ensure seamless deployment of daily Jenkins jobs for delta detection, image captioning, cost/latency guardrails, and rollback strategies.
  • Establish Model and Prompt Governance: Develop and implement policies for prompt templates, few-shot libraries, safety filters, and evaluation rubrics (faithfulness, coverage, compile success).
  • Lead Architecture for UI Onboarding Tool: Design and implement a user-friendly interface for onboarding users to the RAG system, including hosting options, SSO/auth flows, token streaming, and feedback mechanisms.
  • Ensure Data Security and Compliance: Embed privacy policies, source citations, audit logs, and ensure Confluence/Qtest credentials are managed securely.
  • Provide Technical Leadership: Mentor junior engineers and review PRs to ensure adherence to code quality standards.
Must-Have Qualifications:
  • 6–8 years of experience building data or ML platforms, with at least 2 years deploying LLM/RAG systems in production.
  • Deep expertise in LangChain, ChromaDB, Qdrant, or pgvector, and cross-encoder rerankers.
  • Strong proficiency in Python (FastAPI or Flask) and ability to analyze Java codebases for chunking boundaries.
  • Proven experience designing CI/CD pipelines (Jenkins, GitHub Actions) with delta builds and artifact promotion.
  • Hands-on experience managing OpenAI/Anthropic API keys or self-hosting large models.
  • Demonstrated expertise in security and compliance, including PII protection, role-based access, and secret rotation.

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