AI Security Engineer — Secure LLMs & ML Pipelines (Remote)

Applied Systems; United States; On-site; USD 80,000 - 120,000; Full time

Benefits offered by this job

Medical, Dental, and Vision Coverage

Birthday Bonus Day

Remote work option

Job summary

Applied Systems is seeking an experienced security engineer to build and secure its AI security program for LLMs, generative AI, and ML infrastructure. You will apply traditional security principles to AI threats such as prompt injections and data privacy in training pipelines, while growing expertise in an emerging domain.

You will work either from an Applied Systems office or 100% remotely, collaborating with cross-functional teams to shape how AI security is approached across products and

Qualifications

• Minimum of 3-5 years' experience in security engineering or DevSecOps roles.
• Foundation understanding of machine learning concepts, ML workflows, and common frameworks.
• Working knowledge of Large Language Models, transformer architectures, and generative AI applications.
• Experience with or strong understanding of LLM security concerns (prompt injection, jailbreaking, data poisoning).
• Experience with container security, Kubernetes security, and securing AI workloads in containers.
• Knowledge of cloud security in AI contexts (GPU security, distributed training security, data protection in ML pipelines).
• Understanding of secure software development practices and supply chain security as applied to ML models.
• Knowledge of model governance, versioning, and secure model deployment.
• Experience with infrastructure-as-code technologies (Terraform, Ansible).
• Excellent written and verbal communication skills.

Responsibilities

• Evaluate and assess the security posture of Large Language Models (LLMs) and generative AI systems used within or by Applied Systems.
• Conduct threat modeling and security architecture reviews for AI/ML systems and their data pipelines.
• Implement and maintain security controls for AI model training, fine-tuning, and inference infrastructure.
• Develop and maintain security baselines and hardening configurations for AI platforms and frameworks.
• Identify and remediate security vulnerabilities specific to AI/ML systems (prompt injection, model poisoning, data exfiltration, adversarial attacks).
• Implement data security and privacy controls for AI training datasets and inference inputs.
• Develop and maintain security runbooks for AI systems incident response.
• Participate in security reviews of AI/ML applications and vendor AI services.
• Contribute to development of internal AI security policies and guidelines.
• Assist with proof-of-concept builds for AI security solutions and controls.
• Stay current with emerging AI security threats, research, and industry best practices.
• Contribute to internal security training related to AI risks and secure AI development.

Skills

Security engineering

DevSecOps

Machine learning concepts

Python

Bash

Go

Cloud security

Container security

Kubernetes security

Threat modeling

Data privacy

Secure SDLC

Encryption / key management

Communication skills

Tools

Terraform

Ansible

CI/CD

Job description

Applied Systems is seeking an experienced security engineer to build and secure its AI security program for LLMs, generative AI, and ML infrastructure. You will apply traditional security principles to AI threats such as prompt injections and data privacy in training pipelines, while growing expertise in an emerging domain.

You will work either from an Applied Systems office or 100% remotely, collaborating with cross-functional teams to shape how AI security is approached across products and