Teams at DEFCON AI

Build and own production entity-resolution capabilities, including blocking, candidate generation, pairwise scoring, clustering, threshold policies, deduplication, and known-entity checks. Develop provenance systems for traceable, explainable graph relationships and matching decisions. Evaluate matching performance, document implementation logic, and balance false merges against false splits. Work remotely with occasional travel while supporting government mission applications.

Build and operate production retrieval-augmented generation systems, including embeddings, vector retrieval, grounded language-model generation, citation validation, prompt and schema design, model packaging, serving, rollback, and telemetry. Develop bounded model assistance for narrative extraction with provenance and rule-context tracking. Maintain resilient serving paths in secure government cloud environments, including restricted or air-gapped deployments.

Design and build a durable data storage platform that preserves source disagreements, provenance, uncertainty, historical versions, and evidence. Develop result and evidence stores, lineage tracking, versioned rules and configurations, reproducible decision records, and point-in-time entity views. Collaborate with entity-resolution and machine-learning teams to support probabilistic outputs. The role requires production data engineering experience, strong Python and SQL, PostgreSQL expertise, U.S. citizenship, and an active Secret clearance.