Software Engineer (MBSE Plug-in Development)
$85,000–$100,000 year
On-siteArlington, Virginia, United States
Job Summary
Develop and maintain Java-based plugin functionality for Cameo Systems Modeler and MagicDraw, implementing SysML model generation capabilities and building parsers for structured JSON inputs. Collaborate with AI engineering teams to align plugin behavior with LLM output expectations and improve robustness of AI-assisted generation workflows. Diagnose and resolve issues across UI, parsing, API integration, and backend logic while contributing to code quality improvements and testing strategy across the multi-repository codebase. Support iterative feature delivery and coordinate with cross-functional teams to ensure plugin capabilities align with modeling workflows. This role supports mission-critical defense and technology initiatives at G2 Ops, integrating software engineering with model-based systems engineering and digital engineering capabilities.
Required Qualifications
- Bachelor's degree in Computer Science, Software Engineering, or related technical field
- 3+ years of professional Java development experience
- Experience designing and maintaining object-oriented systems in production codebases
- Strong proficiency with JSON parsing/serialization and schema-driven data models
- Ability to debug complex workflows across UI, parsing, API integration, and backend logic
- Experience integrating software with REST or HTTP-based APIs
- Strong communication skills and ability to collaborate across technical teams
- Ability to obtain and maintain required security clearance
Desired Qualifications
- Experience building plugins for Cameo Systems Modeler, MagicDraw, or similar modeling tools
- Familiarity with SysML, MBSE, and model-based engineering concepts
- Experience with modeling tool APIs, sessions/transactions, and diagram automation
- Experience with Eclipse-based plugin environments or JavaFX/Swing UI development
- Experience integrating AI/LLM capabilities into engineering workflows
- Familiarity with prompt/schema design for structured LLM output
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