Principal Systems Engineer

• Lead the design, development, and governance of the Looking Glass Digital Engineering Ecosystem (DEE) architecture end-to-end.
• Define and enforce the DEE framework that connects Program Authoritative Sources of Truth (ASoTs) — data, models, documents — across all stakeholder communities.
• Identify and integrate AI-enabled capabilities into the DEE where appropriate — including intelligent data pipelines, automated anomaly detection, and predictive analytics on program data.
• Ensure DEE architecture enables digital engineering and sustainment access throughout the full program lifecycle.
• Own architectural decisions and produce authoritative architecture artifacts: ConOps, SRDs, SoS architecture diagrams, interface documents, and SoS-level agreements.

• Lead or co-lead the IT technical baseline; develop and deliver the Systems Engineering Management Plan (SEMP).
• Apply Model-Based Systems Engineering (MBSE) using Cameo, Orbus, or equivalent — producing models that are consumable by both engineering and government stakeholders.
• Drive requirements definition, allocation, traceability, and management throughout the program lifecycle using DOORS and/or RTM.
• Lead and prepare program review artifacts for SRR, PDR, CDR, SVR, and TRR; present to IPT and government customer.
• Conduct trade studies, functional analysis, and market research to inform design decisions.

• Architect and implement scalable, redundant, highly available IT solutions across on-premises, co-location, and cloud environments.
• Lead VDI solution architecture and deployment (Nutanix, VMware Horizon) in support of DEE endpoints.
• Design and implement secure operating systems, networks, and COTS software solutions aligned to DEE requirements.
• Optimize cloud resource utilization for cost and performance; lead cloud migration planning as applicable.

• Evaluate and recommend AI/ML tools, platforms, and frameworks appropriate for integration into the DEE — including predictive analytics, anomaly detection, and process optimization use cases.
• Support the design of data pipelines and infrastructure (data lakes, model serving layers) that enable AI/ML workloads within compliant government IT boundaries.
• Apply AI-assisted automation to systems engineering workflows — including requirements analysis, test coverage assessment, and configuration management anomaly detection.
• Assess and document AI/ML system risks, including data governance, model explainability, and compliance with applicable DoD AI ethics guidelines (DoD AI Adoption Strategy, CDAO frameworks).
• Collaborate with data scientists, software engineers, and program stakeholders to translate AI capability needs into actionable DEE infrastructure requirements.

• Ensure DEE architecture and all technology solutions comply with Enterprise policies, Government requirements, and cyber compliance mandates (DFARS, NIST 800-53, CMMC 2.0).
• Lead or oversee the Infrastructure Configuration Control Board (CCB) process; ensure approved change management processes are followed.
• Own configuration management for all DEE hardware, software, and firmware; drive patch and upgrade compliance.
• Develop and maintain the Information Systems Security Plan (ISSP) for production and DEE systems.
• Lead periodic risk assessments; produce documented mitigations and present findings to program leadership.

• Serve as the primary IT/DEE technical interface to the government customer and SE&A IPT Lead.
• Drive collaboration across IT, cyber security, business management, and external vendors to ensure program milestones are met.
• Interface regularly with project managers, architects, technical specialists, and site directors.
• Mentor T01/T02 engineers; serve as technical consultant to IPT leads and program managers.
• Communicate complex technical content — written and verbal — to both technical and non-technical stakeholders.

• Work with IT Asset Management (ITAM) to maintain authoritative records for all DEE-managed hardware and software.
• Author and maintain SOPs, KBs, SRDs, interface documents, V&V plans, ConOps, and DR procedures.
• Track and enforce backup procedures, disaster recovery plans, and patch compliance across the DEE.

• 5 Years of relevant experience with a Bachelors degree in Engineering, Computer Science, or related STEM discipline; 3 Years with a Masters degree; 1 Year with a PhD.
• Candidates must have a current U.S. Government Secret level security clearance (or higher) in order to be considered
• 5–8 years in IT Systems Engineering, Architecture, or Defense IT.
• Demonstrated experience designing or contributing to a Digital Engineering Ecosystem or similar program-level IT architecture.
• Proficient in MBSE tools (Cameo, Orbus, or equivalent); experience producing program-grade models.
• Hands-on experience with DOORS and/or requirements traceability matrices (RTM).
• Working knowledge of NIST 800-53, CMMC 2.0, DFARS, and applicable DoD IT standards.
• Demonstrated ability to lead working groups, technical interchange meetings, and present at formal program reviews.
• Familiarity with AI/ML concepts, tools, or platforms; experience applying AI-enabled automation or analytics in an engineering or IT context is a plus.

• Candidates must have the ability to obtain, and maintain, a U.S. Government Top Secret level security clearance as a condition of continued employment.
• Candidates must have the ability to obtain, and maintain, SCI level access as a condition of continued employment.
• Experience designing data/model governance frameworks that ensure stakeholder access to authoritative program data across the lifecycle.
• Familiarity linking MBSE tools (Cameo, Orbus) to DEE infrastructure — PLM, PDM, document management, and collaboration platforms.
• Understanding of digital thread architecture and how DEE enables program-wide traceability from requirements through sustainment.
• Proficiency with VMware Horizon, Nutanix, or equivalent — including deployment and lifecycle management for DEE endpoints.
• Hands-on experience with AWS, Azure, or GCP; cloud cost optimization; hybrid cloud architecture in classified/unclassified environments.
• Advanced knowledge of TCP/IP, VLANs, routing, firewall architecture, and network segmentation for enterprise environments.
• Experience with on-premises and co-location design; HA, redundancy, backup, and disaster recovery planning.
• MBSE Tools — Cameo Systems Modeler, Orbus iServer, or equivalent (T04: advanced user/model owner preferred).
• Requirements Management — DOORS, DOORS Next Generation, or RTM-based approaches; traceability across lifecycle milestones.
• Program Reviews — Experience leading or co-leading SRR, PDR, CDR, SVR, TRR artifact preparation and presentations.
• Risk Management — Formal risk identification, documentation, and mitigation tracking in program risk registers.
• Working knowledge of supervised/unsupervised learning concepts, model training pipelines, and common frameworks (TensorFlow, PyTorch, scikit-learn, or equivalent).
• Familiarity with AI/ML deployment patterns — model serving, inference infrastructure, and MLOps practices within enterprise IT environments.
• Experience or interest in applying AI-assisted tools to SE workflows: requirements analysis automation, NLP-based traceability, intelligent test coverage, or MBSE model generation support.
• Understanding of how large language models (LLMs) and generative AI tools can responsibly augment engineering productivity within a classified or government-adjacent environment.
• Familiarity with predictive models, anomaly detection on data streams, or AI-driven decision-support tools integrated into program operations.
• Experience surfacing AI/ML outputs through dashboards or operational tools used by engineering or program personnel.
• Understanding of data lake / data warehouse architecture, ETL/ELT pipelines, and the infrastructure requirements for training and serving AI/ML models.
• Awareness of data governance, lineage, and quality requirements that underpin trustworthy AI — particularly within DoD and ITAR-sensitive environments.
• Familiarity with the DoD AI Adoption Strategy, CDAO Responsible AI guidelines, and applicable AI risk assessment frameworks.
• Ability to identify and document risks associated with AI system deployment: explainability, bias, data poisoning, and adversarial robustness.

We offer flexible work arrangements, phenomenal learning opportunities, exposure to a wide variety of projects and customers, and a very friendly team environment.

At Northrop Grumman, we are on the cutting edge of innovation.

Our diverse portfolio of programs means there are endless paths to cultivate your career.

We also offer exceptional benefits/healthcare, a 9/80 work schedule, and a great 401k matching program.

Come join us!