Northrop Grumman is seeking a Senior Principal AI Engineer to join the Data & AI Governance & Strategy organization. This role will help design and operationalize the technology capabilities required to govern AI across the enterprise.
Role Overview
The Senior Principal AI Governance Engineer will sit at the intersection of AI development, data security, privacy, risk, compliance, and enterprise architecture. In this role, you will translate AI governance requirements into scalable technical controls, automation, and observability that can be embedded across AI capabilities and platforms.
You will report to the Manager, Data & AI Governance & Strategy and collaborate closely with Privacy, Legal, Cybersecurity, Global Supply Chain, and other governance bodies to ensure AI systems are designed, deployed, and operated in a safe, compliant, and responsible manner.
The primary location for this position is Falls Church, VA; alternative work locations/arrangements will be considered, including 100% virtual/remote, in accordance with business needs and management approval.
Responsibilities include, but are not limited to:
- Design and implement technical capabilities that support the AI Governance Framework throughout the AI lifecycle, from intake and registration through monitoring and observability.
- Translate AI policies, standards, and requirements into technical controls and automated governance workflows.
- Develop mechanisms and tooling to identify, register, classify, and assess AI use cases, and drive a common taxonomy for evaluation across the enterprise.
- Establish governance patterns for AI capabilities and develop reusable governance components that can be embedded into AI development and deployment pipelines.
- Automate and define standards for evidence collection and documentation to support governance, audit, and assurance needs.
- Design and implement technical guardrails and best practices for AI systems, including usage constraints, access patterns, and safeguards.
- Partner with Privacy, Legal, Cybersecurity, Global Supply Chain, and other governance bodies to integrate existing enterprise controls into AI systems and workflows.
- Develop or scale capabilities that provide enterprise visibility into AI systems and their lifecycle, including the detection and management of Shadow AI.
- Implement mechanisms for ongoing monitoring and observability of AI systems rather than relying solely on point-in-time assessments.
- Contribute to the continuous improvement of AI governance practices, patterns, and tooling across the organization.
Basic Qualifications
- Must have a Bachelor’s degree in Computer Science, Information Systems, Engineering, or related field with 8 years of relevant experience or a Master’s degree with 6 years of relevant experience.
- Experience designing, building, or integrating AI/ML systems or data-driven applications in an enterprise environment.
- Experience with data security, privacy, cybersecurity, or related control environments.
- Demonstrated experience translating governance, risk, or compliance requirements into technical controls, automated workflows, or monitoring/observability solutions.
- Hands-on experience implementing technical guardrails or standards for complex systems (e.g., service-level policies, access control, audit logging, monitoring, or approvals).
- Experience working with cross-functional stakeholders, such as Privacy, Legal, Cybersecurity, Compliance, or Enterprise Architecture.
- Strong analytical, problem-solving, and communication skills with the ability to explain complex technical and governance concepts to both technical and non-technical audiences.
Preferred Qualifications
- Experience designing, implementing, or operationalizing an enterprise AI Governance Framework across the full lifecycle (intake, registration, classification, risk assessment, monitoring, observability).
- Experience developing reusable governance components (e.g., templates, SDKs, APIs, pipelines, policies-as-code) that can be embedded into AI development and deployment workflows.
- Experience establishing common taxonomies and classification schemes for AI or data use cases, including risk categorization and tiering.
- Experience implementing ongoing monitoring and observability for AI systems, including detection and management of Shadow AI and unapproved tools.
- Experience with enterprise governance, risk, and compliance platforms, specifically:
- Credo
- OneTrust
- Experience with AI and data platforms and tooling such as Databricks and IBM WatsonX (or similar enterprise AI platforms).
- Experience with international AI governance, including:
- Emerging AI regulations (e.g., elements aligned with the EU AI Act or other international frameworks)
- Operating AI governance in multi-jurisdiction, global environments
- Familiarity with AI safety, model risk management, or Responsible AI practices (e.g., fairness, transparency, accountability, robustness).
- Experience in large, complex, and/or highly regulated industries (e.g., defense, aerospace, financial services, healthcare).
- Experience with cloud platforms (e.g., AWS, Azure, GCP) and modern data/ML engineering practices (e.g., MLOps, DevSecOps, infrastructure-as-code).
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Azusa, United States
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Camarillo, United States
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Carson, United States
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Fullerton, United States
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Hawthorne, United States
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Huntington Beach, United States
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Laguna Hills, United States
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Los Angeles, United States
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Los Angeles, United States
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Manhattan Beach, United States
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Oxnard, United States
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Pico Rivera, United States
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Pomona, United States
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Port Hueneme, United States
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Redondo Beach, United States
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Santa Monica, United States
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Torrance, United States
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