Data engineer AI systems

act digital designs semantic data layers and knowledge base solutions that power enterprise-scale artificial intelligence systems.

• Design graph schemas, entity resolution strategies, ontological structures, and semantic indexing systems to support agentic AI
• Ingest and transform structured and unstructured data from S3, SharePoint, File Net, and other sources while ensuring semantic consistency
• Create taxonomies and ontologies using RDF, OWL, and SKOS standards
• Operate and orchestrate AWS services including S3, Glue, Aurora PostgreSQL, Neptune, and AWS Bedrock
• Build RAG pipelines, semantic search, metadata enrichment, and Text-to-SQL interfaces
• Design graph retrieval strategies combining pattern matching, vector search, full-text search, and relational traversals
• Ensure quality control, monitoring, and production readiness of AI data infrastructure

• 4–5 Years of experience in Data Engineering, Software Engineering, or AI Engineering with solutions deployed in production
• Advanced Python (P3 - Advanced) and SQL proficiency
• Practical experience modeling and developing queries with Gremlin, SPARQL, and/or Cypher
• Solid AWS experience with S3, Glue, and Aurora PostgreSQL
• Experience designing batch and incremental ETL/ELT pipelines
• Experience with Docker, CI/CD, and Git
• Experience with hybrid retrieval architectures combining SQL, graph, and vector search
• Knowledge of data lineage, cataloging, metadata management, and data quality frameworks
• Degree or Master's in Computer Science, Mathematics, Physics, Engineering, or a related field
• Spanish at C1 (Expert) level
• English at B2 (Advanced) level
• Nice to have:
  Neptune and AWS Bedrock

Hybrid schedule with 40% telework / remote work;
Office schedule.