Senior Principal Software Engineer

Key Responsibilities

• Design, build, and maintain full-stack features across the iMBSE platform, including user-facing web applications, orchestration APIs, and backend microservices.
• Develop and deploy agentic AI solutions, such as LLM-driven workflows, RAG (Retrieval-Augmented Generation) services, and autonomous chat agents.
• Collaborate cross-functionally with product owners, domain experts, and other engineers to deliver extensible and secure solutions supporting MBSE workflows.
• Contribute to system architecture, ensuring well-defined interfaces, low coupling, and future extensibility.
• Ensure compliance with DevSecOps best practices, leveraging CI/CD pipelines and automated testing, security scanning, and cloud-native tooling (see “Our Tech Stack” below).
• Mentor junior engineers and champion engineering excellence through code reviews, design discussions, and knowledge sharing

Minimum Qualifications

• BS 10-12, MS 8-10, PhD 5-7
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a closely related field.
• 5+ years of professional software engineering experience, including significant full-stack development.
• Extensive hands-on experience with Python and/or Rust in production environments.
• Proven track record building, deploying, and scaling agentic AI solutions (LLM agents, RAG pipelines, etc.) using frameworks such as LangChain, LlamaIndex, or similar.
• Proficiency with modern frontend technologies (React, Redux, JavaScript/TypeScript, HTML/CSS) and contemporary backend architectures (REST APIs, microservices).
• Strong understanding of cloud infrastructure and platforms (AWS, Azure, Kubernetes, Docker).
• Experience developing and maintaining CI/CD pipelines (GitLab, Docker, Maven), and automated testing frameworks (pytest, JUnit).
• Solid grasp of secure coding practices and DevSecOps workflows
• Ability to obtain/maintain Secret Clearance .

Preferred Qualifications

• Experience integrating with engineering and MBSE tools (e.g., Cameo Systems Modeler, Teamwork Cloud).
• Familiarity with storage architectures (graph DBs, relational DBs, blob storage), API-driven platforms, and event-driven workflows.
• Experience with AI model orchestration (e.g., LLM/embedding model lifecycle, logging, compliance, RBAC).
• Knowledge of system observability tools (Prometheus, Grafana) and automated monitoring/alerting.
• Exposure to state-of-the-art LLMs (OpenAI GPT-4, Meta Llama 3, etc.) and related AI APIs

Technology and Tools