Cognitive Knowledge Engineer

About the role:
We are looking for a Cognitive Knowledge Engineer — a unique blend of Data Engineer and LLM expert. This is a senior and strategic position that requires a rare combination of skills: strong expertise in traditional data engineering and deep knowledge of modern AI technologies. The main mission is to design and build the future data architecture that will serve as the foundation for the company’s AI initiatives.

Core Responsibilities:

• Leading strategy and decision-making for large-scale production data environments.
• Designing and implementing solutions for multimodal storage systems and knowledge graphs.
• Evaluating and integrating graph databases alongside the existing Postgres setup.
• Bridging the gap between traditional knowledge representation techniques (e.g., SPARQL, RDF, semantic technologies) and modern LLM capabilities.
• Collaborating closely with another Knowledge Engineer to validate ideas and solutions.

Requirements:

• Strong background in Data Engineering combined with practical LLM experience.
• Deep understanding of knowledge representation and graph technologies (SPARQL, RDF).
• Proven experience in organizing and managing data at scale in production environments.
• Ability to lead technical discussions and make architectural decisions at a strategic level.

Ideal Candidate Profile:

• Essentially a Data Engineer with deep LLM expertise.
• A strategic thinker able to design long-term AI-driven architectures.
• Strong communicator, capable of articulating complex ideas and decisions.
• Team player, comfortable working in tandem with another Knowledge Engineer.
• Builder mentality — prefers designing and implementing systems rather than sitting in endless meetings.

What we offer

• A strategic role at the intersection of Data Engineering and AI innovation.
• The opportunity to define the core data architecture for future AI products.
• A fully remote international team (Europe + US EST).
• Flexible work setup and a culture that values initiative and ownership.
• Transparent and efficient hiring process (4 steps).

Hiring Process

1. Intro call with recruiter (up to 30 min) — basic screening.
2. Live Technical Challenge (1 hour) — live coding with a proctor. Google allowed, no AI assistants.
3. Interview with Head of AI (30 min) — culture, work style, expectations.
4. Final Team Interview (1–2 hours) — open problem-solving session, focusing on reasoning and teamwork.