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Há 20 horas
We are seeking a highly skilled Senior AI Engineer to lead the design and implementation of an end-to-end Retrieval-Augmented Generation (RAG) architecture. This role will drive the ingestion of various data sources, including GitHub repositories, Confluence pages, Qtest artifacts, PRDs, and script libraries, to power autoscripting, onboarding search, and long-term knowledge reuse.
Key Responsibilities:- Design and architect ingestion and retrieval layers, selecting cutting-edge models and technologies such as LangChain, ChromaDB, Qdrant, or pgvector, and cross-encoder rerankers.
- Create CI orchestrations, including daily Jenkins jobs for delta detection, image captioning, cost/latency guardrails, and rollback strategies.
- Establish model and prompt governance, including prompt templates, few-shot libraries, safety filters, and evaluation rubrics.
- Lead the architecture for a UI onboarding tool, deciding on hosting options, SSO/auth flows, token streaming, and feedback mechanisms for continuous learning.
- Oversee data security and compliance, embedding privacy policies, source citations, audit logs, and ensuring secure management of credentials in Secrets Manager.
- Provide technical leadership by reviewing pull requests, setting code quality standards, and conducting architecture workshops for AI and Automation Agent Engineers.
- 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 and ability to analyze Java codebases.
- Proven experience designing CI/CD pipelines 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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