Sr Principal Software Engineer - AI Software

Northrop Grumman Northrop Grumman · Aerospace · San Diego, CA +4 · Software

Senior Principal Software Engineer focused on AI Software for defense applications. The role involves leading the strategy, architecture, and implementation of secure, reliable, and scalable AI systems, with a strong emphasis on production delivery, integration into enterprise systems, and scaling for performance in regulated environments. Experience with LLM solutions, RAG, guardrails, and MLOps is preferred.

What you'd actually do

  1. Provide collaborative technical leadership that drives the maturation of Artificial Intelligence solutions across a portfolio of contracts and operating units
  2. Partner with product, platform, data, security, and compliance to establish responsible AI practices, model governance, and enterprise-grade MLOps.
  3. Act as the Subject Matter Expert for all aspects of the enterprise Artificial Intelligence roadmap and execution.
  4. Provide senior technical leadership thorough design reviews, prototyping, code contributions, and mentoring while aligning roadmaps across teams.

Skills

Required

  • Bachelor’s Degree in STEM with 8 years of experience, or Master’s with 6, or PhD with 4
  • Professional software engineering experience (Python, Java, C++)
  • Applied AI/ML engineering experience delivering models to production
  • Experience integrating AI/ML capabilities into enterprise software systems
  • Experience working in regulated environments
  • Demonstrated technical leadership
  • Active U.S. Government Top Secret clearance
  • Ability to obtain and maintain initial Special Program Access (SAP/PAR)

Nice to have

  • Current applicable Special Access Program (SAP) clearance
  • Advanced degree (MS/PhD) in Machine Learning, Computer Science, or related field
  • Modern software practices (version control, code reviews, automated testing, CI/CD, containerized deployment)
  • MLOps fundamentals (model evaluation, reproducibility, monitoring/observability, model lifecycle)
  • Leading AI technical strategy/roadmaps
  • Deep experience with LLM solutions (RAG, evaluation/guardrails, enterprise integration)
  • Strong MLOps experience (pipelines/orchestration, experiment tracking, model registry, automated retraining, drift detection)
  • Experience scaling AI systems for reliability/performance
  • Secure DevSecOps practices and supply-chain security
  • Familiarity with enterprise AI governance and responsible-use expectations

What the JD emphasized

  • Active U.S. Government Top Secret clearance
  • Experience working in regulated environments with strong emphasis on security, data handling, and governance expectations for AI use.
  • Applied AI/ML engineering experience delivering models to production
  • Experience integrating AI/ML capabilities into enterprise software systems
  • Experience scaling AI systems for reliability/performance

Other signals

  • delivering models to production
  • integrating AI/ML capabilities into enterprise software systems
  • scaling AI systems for reliability/performance