Senior AI Engineer, Security Infrastructure
New
A
AirEnterprise Readiness
Arlington, Virginia, United States; Pittsburgh, Pennsylvania, United States; RemoteFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- AWSPythonGCPKubernetesAzureDistributed Systems
Requirements
- 5+ years of experience building production software, backend infrastructure, distributed systems, security systems, or AI/ML infrastructure.
- Bachelor's, Master's, or Doctorate in Computer Science, Computer Engineering, Cybersecurity, Data Science, or a related field, or equivalent experience.
- Demonstrated experience in AI red teaming, adversarial machine learning, offensive security, or systems security research.
- Strong understanding of modern LLM and agentic systems, including model inference, context management, and tool use.
- Strong intuition for how AI systems fail when exposed to adversarial users and untrusted data.
- Proficiency in Python and building production-quality software.
- Experience operating production services on Kubernetes and cloud platforms such as AWS, GCP, or Azure.
- Strong understanding of networking, distributed systems, containers, service orchestration, and scalable architectures.
- Experience designing APIs, services, and event-driven architectures.
- Ability to debug failures across application code, models, and infrastructure.
Responsibilities
- Research and develop new approaches to AI red teaming, adversarial testing, security evaluation, and robust inference.
- Threat model agentic AI architectures, identifying trust boundaries, attack surfaces, privileged capabilities, and potential failure modes.
- Design adversarial evaluations targeting threats such as prompt injection, tool abuse, privilege escalation, and data exfiltration.
- Build automated security evaluation and regression frameworks that continuously test agents, models, and tools.
- Translate research findings into production mitigations, architectural improvements, and reusable security controls.
- Design secure execution environments, sandboxing, and isolation mechanisms for untrusted agent workloads.
- Own and improve production infrastructure across Kubernetes, AWS, networking, and compute.
- Implement security controls around identity and access management, secrets, and container security.
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