Big data and ML platform for federal government search and intelligence
BTI360 builds distributed search, machine learning, and cloud infrastructure for federal agencies. The stack reveals a mature, production-grade operation: Solr/Elasticsearch for search, Kafka/Kinesis for streaming, Kubernetes on AWS for orchestration, and LangChain for LLM integration. Active hiring skews engineering-heavy (9 of 15 roles), with concurrent projects spanning rapid LLM prototyping, mission-critical data discovery platforms, and Databricks pipeline optimization—suggesting a shift toward AI-augmented analytics while maintaining core search and data infrastructure.
Notable leadership hires: Technical Lead
BTI360 is a Washington DC–based software development firm founded in 2004, serving federal government agencies with specialized solutions in distributed search, machine learning, and cloud engineering. The company operates across three primary technical domains: search systems (Solr, Elasticsearch, OpenSearch), streaming and batch data pipelines (Kafka, Kinesis, Databricks), and cloud-native deployment infrastructure (AWS, Kubernetes, Terraform). Projects focus on reducing analyst time spent on content discovery and improving data quality, security compliance, and operational intelligence. The organization operates at 51–200 employees with a stated emphasis on technical excellence and team development.
Python, Node.js, Java-based search (Solr, Elasticsearch, OpenSearch), Kafka/Kinesis for streaming, Kubernetes on AWS, LangChain for LLM work, and Terraform for infrastructure-as-code. Data pipeline tooling includes Databricks, Spark, and Cassandra.
End-to-end ML projects, rapid LLM prototyping, mission-critical data discovery platforms, cloud-native architecture, Databricks pipeline design, and CI/CD automation. Focus areas include data quality optimization, secure infrastructure, and federal compliance.
BTI360's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.