Knowledge Graph Software Engineer

About the Team

Here at BTI360, we’ve built a culture that’s passionate about developing software engineers. Software doesn't build itself. People do.  In fact, teams of people do.  That's why our primary focus is on developing better craftsmen, better teammates, and better technical leaders.  By putting people first, we're not just giving our teammates more opportunities to grow, we're also raising the bar of the software we ship. BTI360 previously has been voted 10 years in a row as a TOP Place to Work by the Washington Business Journal. 

BTI360 is seeking an Knowledge Graph Software Engineer who is passionate about transforming raw data into meaningful insights that drive strategic decision‑making. In this role, you’ll work closely with our engineers and mission teams to tackle complex business problems using emerging AI technologies — specifically by extracting and linking critical entities to build and curate scalable graph databases and knowledge bases. This role sits at the intersection of backend software engineering, knowledge representation, and applied AI.

 

What you will do in this role:

As a Knowledge Graph Software Engineer at BTI360, you will:

• Lead end-to-end knowledge graph and knowledge base development efforts from problem definition to production, designing pipelines that extract, normalize, link, and organize information into scalable graph-based systems. 
• Design and evaluate extraction and resolution workflows using sound methodologies and fit-for-purpose metrics to assess entity extraction, linking, relationship quality, and overall knowledge base completeness and accuracy. 
• Translate business requirements into quantitative problems and communicate technical findings to both technical and non-technical stakeholders through reports, presentations, and direct customer engagement. 
• Drive technical decision-making for schema design, ontology alignment, extraction approaches, and graph architecture based on mission needs, data quality, and long-term maintainability. 
• Stay current with advances in knowledge representation and information extraction and introduce practical techniques, tools, and frameworks that improve graph construction, curation, and analytic value. 
• Apply analytical and statistical methods to validate extracted insights, measure data quality, and support confident decision-making from structured and unstructured sources. 
• Develop reports and whitepapers that evaluate solution alternatives based on impact, cost, technical feasibility, and alignment with strategic goals. 
• Collaborate across teams to align on strategy, provide data science expertise, and contribute to proposals and strategic initiatives.