We are seeking an AI Engineer to build the AI core of a new B2B platform being delivered to a client. Client and project details are confidential and will be disclosed at onboarding under a confidentiality agreement.
The systems operate in a domain where an incorrectly extracted value is a serious defect: accuracy, grounding and controllability take priority over raw capability. The role covers production LLM engineering end-to-end: retrieval-augmented generation over a restricted document corpus with strict source boundaries, document and PDF data-extraction pipelines that normalize inconsistent real-world specifications, NLP classification pipelines, semantic search, and conversational intake that converts informal user language into accurate structured data. The engineer works within a small senior delivery team - a Solution Architect who owns the technical design,
and a Senior Full-Stack Developer who consumes the engineer's APIs - and demonstrates completed work in fortnightly sprint reviews with client stakeholders present.
EolasFlow is an AI-native engineering team: AI-assisted development (Claude Code, Cursor, GitHub Copilot or equivalent) is the standard working method, and candidates are expected to already work this way.
Key Responsibilities
• Design and build a production RAG system: chunking and embedding strategy, vector store, retrieval evaluation, citation-grounded answering, and strict source-boundary enforcement with refusal on out-of-bound queries
• Build document-intelligence pipelines: PDF and table extraction from inconsistent source documents, unit and format normalization, deduplication, and human-audit workflows
• Build NLP pipelines for content classification (signal vs noise), entity extraction and enrichment, and automated draft generation matched to a defined editorial voice
• Build semantic search mapping natural-language intent to structured capabilit