A-GOS is a VC-backed AI startup building an intelligent procurement and catering platform for the maritime industry.
We are now looking for an AI Engineer with practical experience in knowledge graphs, ontologies, and AI agents.
The Challenge
One of our main tasks is generating optimal weekly menus for vessel crews.
The system must consider:
Ingredients available on board
Quantities, prices, and expiration dates
Crew nationalities and dietary, religious and cultural restrictions
Consumption and nutritional norms
Budget and food waste
Recipe compatibility and menu diversity
Today, much of this logic is handled through Google OR-Tools. However, even with more than 500 recipes in our database, strict recipe structures often make it impossible to generate an optimal menu.
We want to make the system more intelligent.
It should be able to create valid recipe variations by replacing ingredients only when the substitution truly makes sense. The system should understand the ingredient’s role, taste, texture, cooking method, cuisine, nutrition, restrictions, and compatibility with other ingredients.
What You Will Work On
Building a knowledge graph for ingredients, recipes, cuisines, restrictions, and substitution rules
Designing the domain ontology and semantic model
Developing intelligent ingredient substitution logic
Building AI agents that reason over recipes, inventory, and crew requirements
Integrating the knowledge layer with Google OR-Tools and our existing platform
Creating explainable logic for generated recipes and menu decisions
What We Expect
Practical experience with knowledge graphs or ontology design
Experience developing AI agents or LLM-based systems
Strong Python skills
Understanding of semantic modelling, structured reasoning, and retrieval
Ability to transform complex business rules into a scalable architecture
Strong ownership and product thinking
What matters is that you have built a real system using knowledge graphs or ontologies and can explain your personal contribution.
Experience with Neo4j, RDF, OWL, SPARQL, GraphRAG, LangGraph, LangChain, LlamaIndex, or similar technologies would be a plus.
Why A-GOS
Early-stage VC-backed startup
Real and technically challenging AI product
Direct influence on product and architecture
Remote work and flexible schedule
Small team with minimal bureaucracy
Potential equity based on long-term contribution
How to Apply
Please briefly describe a real project where you worked with knowledge graphs, ontologies, RAG, semantic models, or AI agents.
Tell us what problem you solved, which technologies you used, what you personally built, and how it was integrated into the final system.
