Requirements
Requires 5-10+ years of data engineering experience, with at least 2 years focused on building AI-ready data products. Must have strong knowledge of graph technologies (Neo4j, RDF/SPARQL), semantic modeling, vector/retrieval infrastructure, and data pipeline engineering (Spark, dbt, Airflow). Familiarity with enterprise data governance and lineage tooling is also required.
Description
Systems Ltd is looking for a Forward Deployed Engineer – Data Management to Make enterprise data and knowledge usable by AI — build AI-ready data products, knowledge graphs, and retrieval infrastructure that every other practice depends on.KEY RESPONSIBILITIESBuild AI-ready data pipelines and data products that other practices can consume directlyDesign and build knowledge graphs and semantic layers that structure enterprise knowledge for AI consumptionOwn vector and retrieval infrastructure (embeddings, indexes, hybrid search) shared across GenAI and ML practicesRun data quality assessment and remediation specifically for AI/ML consumption, not just BIOwn knowledge engineering — taxonomy, ontology, and ingestion pipelines for enterprise knowledge sourcesPartner with GenAI Engineers, Data Scientists, and AI Architects to expose curated data/knowledge as reusable assetsExplain the difference between BI-grade and AI-grade data quality to non-technical stakeholdersAct as a shared upstream dependency for multiple practices — manage competing requestsDocument data/knowledge assets clearly enough for self-service reuseREQUIREMENTS & SKILLS5–10+ yrs data engineering, with 2+ yrs building AI-ready data products specificallyStrong in knowledge graph technologies (Neo4j, RDF/SPARQL, or similar) and semantic/ontology modelingExperience with vector/retrieval infrastructure (embeddings, ANN indexes, hybrid search)Solid data pipeline engineering (Spark, dbt, Airflow, or similar) and data quality frameworksFamiliarity with enterprise data governance and lineage toolingCan explain the difference between BI-grade and AI-grade data quality to a non-technical stakeholderCollaborates closely with GenAI Engineers, Data Scientists, and AI Architects as a shared upstream dependencyPrioritizes competing requests from multiple practices fairly and transparentlyDocuments clearly enough that other teams can self-serve without hand-holdingSuccess metrics: data/knowledge asset reuse across practices · data quality incidents affecting AI systems (target zero) · time from raw data to AI-ready asset