Senior DevSecOps Engineer

Job Description:

We are seeking an experienced software engineer to support a small team to design, develop, integrate, and build an AI red teaming platform. This role is focused on developing advanced adversarial AI tools and capabilities in support of the Lockheed AI Center (LAIC) Red Team operating across classified and unclassified environments. This position integrates open source, custom developed, and commercial adversarial AI utilities, provides a unified user interface, and enables automated, repeatable assessments of AI systems across use cases, including generative/agentic, computer vision, and RF/EW.

These AI systems span the deployment spectrum from Lockheed Martin internal enterprise and cloud environments to edge compute platforms. The position involves both planned project work and rapid ad hoc support, with extensive documentation, automation, and occasional travel for field demonstrations.

• Collaborate closely with multiple AI Red Teams to design, architect, and implement adversarial AI capabilities for autonomous agents, computer vision systems, and RF and EW systems, translating state‑of‑the‑art research into production‑ready code.
• Design and build a modular, extensible red‑teaming framework that enables repeatable, user‑driven evaluations.
• Design user-facing interfaces and workflows (CLI, web UI, APIs, CI/CD pipelines) and documentation to enable internal and future third‑party users to execute red team activities with minimal subject matter expertise.
• Integrate open‑source, commercial, and inner‑source solutions into a cohesive platform, handling packaging, versioning, release, dependency management, and maintenance.
• Develop and maintain CI/CD pipelines (Git Lab, Azure Dev Ops, or equivalent) with automated testing, security scans, and deployment to cloud and containerized/VM‑based environments.
• Coordinate with Infrastructure Operations (Infra Ops) stakeholders for virtualized, containerized, and cloud‑based Linux environments, including configuration, orchestration, DISA STIG compliance, and lifecycle maintenance.
• Implement observability (structured logging, metrics, dashboards) using Splunk, Prometheus, Grafana, Lang Fuse, Open Telemetry, and ensure full traceability for debugging, performance analysis, operational reliability, and audit.
• Develop GPU‑enabled software workloads (NVIDIA drivers, CUDA, container runtimes) and continuously optimize performance and stability.
• Champion secure development practices – embed threat modeling, code reviews, and Dev Sec Ops  controls throughout the software lifecycle.
• Coordinate development and deployment activities across classified (on‑site) and unclassified (remote) environments while ensuring secure and reliable operations.

From onsite to remote, we offer flexible work schedules to comprehensive benefits investing in your future and security, Learn more about Lockheed Martin's comprehensive benefits package here.

Do you want to be part of a company culture that empowers employees to think big, lead with a growth mindset, and make the impossible a reality? We provide the resources and give you the flexibility to enable inspiration and focus
-if you have the passion and courage to dream big, work hard, and have fun doing what you love then we want to build a better tomorrow with you.

This position is a hybrid role with mostly remote work, however due to the need for occasional onsite work the candidate will need to be near a major Lockheed Martin site such as Moorestown NJ, Dallas TX, or Orlando FL.

• Hands‑on engineering experience with building tools, frameworks, and automation integrated with Git Lab, cloud, Dev Sec Ops , or CI/CD pipelines.
• Demonstrated experience containerizing applications (i.e., Docker, Podman) and deploying them to scaled Kubernetes/Open Shift environments via infrastructure‑as‑code (e.g., Ansible, Helm).
• Demonstrated ability to author high‑quality documentation (design specs, runbooks, API guides).
• Demonstrated experience developing microservices (preference for Python).
• Experience building AI agents using frameworks (e.g., CrewAI, Auto Gen, Lang Chain/Lang Graph).
• Experience working with deep learning frameworks (e.g., PyTorch, Tensor Flow).
• Familiarity with AI security or adversarial ML.