AI Red Team Manager
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Lockheed Martin
Remote
Full-Time
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Job Description
AI Summary & Insights Summary Insights AI Red Team Manager The insights provided are generated by AI and may contain inaccuracies. Please independently verify any critical information before relying on it. AI Red Team Manager The insights provided are generated by AI and may contain inaccuracies. Please independently verify any critical information before relying on it.
Description Job Description:
We are seeking an experienced software engineer and architect to lead 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 adhoc support, with extensive documentation, automation, and occasional travel for field demonstrations. Key Responsibilities- 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.
- Lead design, development, and operationalization of enterprise-scale tools for evaluation, automation, and CI/CD integration
- Lead and manage small team, task management, project management, and personnel management to achieve the overall teams objectives.
- 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 (GitLab, Azure DevOps, or equivalent) with automated testing, security scans, and deployment to cloud and containerized/VM‑based environments.
- Coordinate with Infrastructure Operations (InfraOps) 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, LangFuse, OpenTelemetry, 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 DevSecOps controls throughout the software lifecycle.
- Coordinate development and deployment activities across classified (on-site) and unclassified (remote) environments while ensuring secure and reliable operations.
LMLAIC Qualifications:
- Experience in developing and delivering software products for enterprise or market deployment.
- Hands-on engineering experience with building tools, frameworks, and automation integrated with GitLab, cloud, DevSecOps, or CI/CD pipelines.
- Demonstrated experience containerizing applications (i.e., Docker, Podman) and deploying them to scaled Kubernetes/OpenShift environments via infrastructure-as-code (e.g., Ansible, Helm).
- Demonstrated ability to author high‑quality documentation (design specs, runbooks, API guides).
- Experience developing microservices (preference for Python).
- Familiarity with AI security or adversarial ML.
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