data engineer for 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, FileNet, 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.