Job Description Summary

Chargé de programmer un composant, une fonctionnalité et/ou un ensemble de fonctionnalités. Travaille de manière indépendante et contribue à l'équipe concernée et à d'autres équipes de l'entreprise. Vous contribuerez également aux discussions sur la conception.

Job Description

Roles and Responsibilities

• Build the AI Context Layer: Develop and maintain backend services and data pipelines that supply relevant business context, metadata, and constraints to LLM applications.
• Work with Semantic Models & Ontologies: Help implement, update, and query domain ontologies and taxonomies using standards like RDF, OWL, or SPARQL under the guidance of senior architects.
• Knowledge Graph Integration: Write code to interact with graph databases (e.g., Neo4j, AWS Neptune) to support semantic search and relationship mapping.
• Enhance Search & RAG Pipelines: Help build and optimize hybrid retrieval systems combining vector search (semantic similarity) with structured graph data for better LLM grounding.
• Data Pipelines: Write robust data ingestion and processing scripts to extract entities and relationships from various enterprise data sources.
• Collaboration: Partner with ML engineers, data scientists, and backend developers to integrate semantic components into production AI products.
• Code Quality: Write clean, testable, and well-documented code following best software engineering practices.
• Required Qualifications & Skills
• Experience: 4–6 years of software development experience, with at least 1–2 years working on data-heavy applications, NLP, vector search, or semantic/graph technologies.
• Programming: Strong backend development skills in Python (preferred for AI workflows) and/or Java/TypeScript.
• Databases & Storage: Experience working with relational databases and at least some exposure to NoSQL, vector databases (e.g., Pinecone, Milvus, Qdrant), or graph databases (e.g., Neo4j).
• Foundational Knowledge of Semantics/AI: Familiarity with concepts like knowledge graphs, vector embeddings, prompt engineering, or semantic web standards (RDF/OWL). We don't expect an ontology expert, but you should be eager to learn and work with them.
• API Development: Experience building and consuming RESTful APIs and microservices.

Education: Bachelor’s degree in computer science, software engineering, or a related technical field (or equivalent practical experience).

Additional Information