AI Data and Ontology Engineer

Title: Senior / Staff Data and Knowledge Engineer

Location: Los Angeles, CA

Entity: Aperium AI Federal

Aperium AI Federal delivers AI orchestration and decision-support platforms to U.S. Government and allied defense customers. Our systems integrate data from many sources into a common operating picture and support analysis and organizational workflows in secure, disconnected environments.

Summary

We are hiring a Senior or Staff Data and Knowledge Engineer to lead the onboarding of multimodal customer data into the platform's governed data and knowledge models. The role covers ingesting structured records, documents, imagery, geospatial data, logs, and sensor feeds; mapping customer schemas into shared concepts; resolving entities across sources; and making the resulting information available to analytics, retrieval, AI workflows, and geospatial applications.

This is a hands-on individual contributor role on an existing product and engineering team. You will write production code regularly, work directly with unfamiliar customer data, and turn recurring onboarding needs into reusable platform capabilities. All work is subject to strict provenance, audit, access-control, and security requirements. The position is on-site and requires U.S. citizenship and eligibility to work on ITAR/export-controlled programs.

Responsibilities

• Onboard customer data across modalities into a common ontology and governed data model.
• Build and operate batch and streaming ingestion pipelines in cloud, on-premises, and air-gapped environments.
• Extend platform connectors to new customer systems, APIs, databases, file formats, and sensor feeds.
• Preserve source structure and provenance so data remains traceable to its originating file, page, table, row, field, system, and timestamp.
• Design schema mapping and normalization workflows that preserve important customer-specific detail.
• Develop entity-resolution, record-linkage, deduplication, and confidence-weighted merge strategies across disparate sources.
• Retain conflicting claims, source reliability, uncertainty, and the history of how resolved information changes over time.
• Build validation, quarantine, replay, and safe reprocessing behavior for malformed, unfamiliar, or changing data.
• Create analyst review workflows for ambiguous matches, mapping exceptions, extraction errors, and conflicting evidence.
• Establish evaluation datasets and metrics for extraction quality, mapping coverage, resolution precision and recall, calibration, and end-to-end onboarding quality.
• Maintain lineage, provenance, tenant isolation, and access controls throughout ingestion, knowledge projection, retrieval, and export.
• Partner with customer-facing engineers on initial adapters and graduate reusable transformations into the shared platform.
• At the Staff level, set data and knowledge engineering standards, define platform architecture, and mentor other engineers.

Requirements

• U.S. citizenship, with eligibility to work under ITAR/export-control programs. Active or prior security clearance is a plus.
• Bachelor's or Master's degree in Computer Science, Data Engineering, Statistics, or a related field, or equivalent practical experience.
• 7+ years building and operating production data platforms across structured and unstructured workloads; exceptional candidates with closely related experience will also be considered.
• Strong Python and SQL skills with production-grade software development practices.
• Production experience with data ingestion, entity resolution, record linkage, master data management, document understanding, or a closely related data-quality domain.
• Experience building batch pipelines and streaming systems using Kafka or an equivalent technology.
• Experience with workflow orchestration tools and reliable reprocessing of data-intensive workloads.
• Experience with knowledge graphs, vector databases, relational databases, and lakehouse or analytical storage architectures.
• Applied experience using LLMs for document processing, schema mapping, entity resolution, or data-quality workflows, with judgment about when deterministic methods are more appropriate.
• Experience defining measurable quality controls for messy real-world data.
• Ability to reason carefully about ambiguity, provenance, temporal change, conflicting sources, and uncertainty.
• Clear, concise written and verbal communication.

Preferred

• Experience with OCR, document layout analysis, table extraction, imagery, or multimodal parsing.
• Experience with geospatial formats, spatial matching, tracks, sensor data, or uncertain geometries.
• Experience with ontology design, graph data modeling, bitemporal systems, or confidence-aware knowledge representation.
• Experience with active learning, human review systems, weak supervision, or confidence calibration.
• Experience with data lineage, data contracts, privacy-aware data platforms, or tenant-isolated storage.
• Experience deploying data systems in restricted, on-premises, edge, or disconnected environments.
• Experience working directly with government or enterprise customers on complex data onboarding.
• Experience in defense, intelligence, logistics, public safety, or another specialized data domain.

This is an exempt position.