Ai Knowledge Engineer

AI Knowledge Engineer & Content Architect - Full Remote Portugal
Job Openings AI Knowledge Engineer & Content Architect - Full Remote PortugalAbout the job AI Knowledge Engineer & Content Architect - Full Remote Portugal
ABOUT THE OPPORTUNITY
A rare and genuinely cutting-edge role at
Switzerland's leading telecommunications challenger brand, now building out their Portugal hub.
This isn't a standard knowledge management or technical writing position — it sits at the frontier of AI engineering, where your work directly determines how well LLMs reason, retrieve, and respond.
If you understand how machines consume information and want to architect that process from the ground up, this is your opportunity.Engagement type:
Direct Hiring |
Level:
Mid-level |
Base salary:
Up to €45,000 (total compensation up to ~€50,000 with bonus and perks) |
Language:
English fluent (required) |
Location:
Portugal-based (Lisbon area preferred)PROJECT & CONTEXT
You'll be the architect behind an internal AI knowledge ecosystem — designing how information is structured, chunked, tagged, and retrieved by LLMs in a production RAG environment.
Working at the intersection of knowledge management and AI engineering, you'll collaborate with SMEs, technical teams, and business stakeholders across Portugal and Switzerland to ensure the organisation's AI agents have the right information, at the right time, with high accuracy.
Full remote, with occasional in-person visits to the Lisbon office for team events or collaborative sessions.WHAT WE'RE LOOKING FOR
Working understanding of
LLM architecture
concepts — particularly
RAG (Retrieval-Augmented Generation), context windows, embeddings, and the distinction between lexical and semantic searchHands-on experience designing and implementing
chunking strategies
(recursive, semantic, fixed-size) to preserve logical coherence within context window constraintsProven ability to design
metadata taxonomies, ontologies, and filtering schemas
that improve AI retrieval accuracy and narrow search spaces effectivelyProficiency in
Markdown, JSON, and YAML, with a clear understanding of how document structure (headers, lists, tables) influences model attention and parsing qualityFamiliarity with
vector databases
such as Pinecone, Milvus, or Weaviate, and knowledge of reranking or hybrid search techniquesExperience in
information architecture
— creating enterprise-grade taxonomies, knowledge graphs, or structured content systems at scaleDemonstrated ability to conduct
hallucination and gap analysis
— identifying missing, conflicting, or ambiguous knowledge and restructuring content to provide reliable ground truthStrong technical writing skills with extreme clarity and precision, eliminating linguistic ambiguity that could lead to model misinterpretationExperience establishing
knowledge lifecycle workflows
— managing version control, resolving contradictions between legacy and updated data, and maintaining a clean, non-redundant datasetAbility to interview SMEs and translate tacit knowledge into structured, explicit logic usable by LLMs ("golden sets")Fluent in English
— mandatory for all stakeholder communication and content creationNICE TO HAVE
Basic scripting skills in
Python
(Pandas, LangChain) or
SQL
for automating data cleaning or querying vector storesExperience with
RAG evaluation frameworks
such as RAGAS, or with analysing search logs to identify retrieval performance bottlenecksProficiency in
German, French, or Italian
— a meaningful advantage given the Swiss operational contextExposure to
access control mapping
and data permission structures within AI knowledge systemsBackground in
Telco
or other complex, regulated industry environmentsExperience building continuous feedback loops between end users and development teams to iteratively improve knowledge assets
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