Applied Systems is building out its AI security program and seeking an experienced security engineer to help secure our Large Language Models, generative AI systems, and ML infrastructure. This is an emerging role for an engineer who understands traditional security principles deeply and is eager to apply them to the rapidly evolving world of AI, from prompt injection and model poisoning to data privacy in training pipelines. You won't need to be an ML researcher or data scientist, but you should have foundational knowledge of how machine learning works and be genuinely curious about AI-specific security threats. This role offers the opportunity to shape how Applied approaches AI security, work with cutting-edge technology, and grow expertise in an area where few security engineers have deep experience. Ideal for someone who values both hands-on security work and the challenge of learning an emerging domain.

• 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 help 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 the 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

We’re looking for someone who:

• Has the ability to work from an Applied Systems office or 100% remotely
• Your experience should include some or all of the following:
  • Minimum of 3-5 years' experience in security engineering or DevSecOps roles
  • Demonstrated foundational understanding of machine learning concepts, ML workflows, and common frameworks (PyTorch, TensorFlow, scikit-learn)
  • 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, model extraction)
  • 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)
  • Experience with one or more scripting languages (Python, Bash, Go)
  • Understanding of encryption, key management, and data privacy (especially PII in training data)
  • Familiarity with vulnerability scanning and secure code practices
  • Working knowledge of compliance frameworks and their application to AI systems (GDPR, SOC 2, etc.)
  • Experience with securing data pipelines and ETL processes
  • Excellent written and verbal communication skills
  • Demonstrated ability to work independently and as part of a team
  • Willingness to rapidly learn new AI technologies and security frameworks
• You may also have:
  • Certification in security (Security+, CISSP, etc.)
  • Experience with specific LLM platforms (OpenAI API, Anthropic Claude, Google Vertex AI, Azure OpenAI)
  • Experience with ML security tools and platforms (Robust Intelligence, Arthur AI, etc.)
  • Participation in AI security research, publications, or conferences
  • Experience in threat modeling for AI systems
  • Background in security research or penetration testing
  • Experience with red-teaming AI systems
  • We know that talent comes from all backgrounds and experience levels. We encourage military members and their spouses as well as candidates without a degree or a background in tech to apply!

Location

Candidate will need to reside in North America, working arrangement will be remote.

When You Join Team Applied, You Can Expect:

A culture that values who you areand recognizes that you arent just an employee; you are a teammate, and you matter. We thrive on the benefits of our different experiences and celebrate the uniqueness our teammates bring to work with them every day.

We flex our time together, collaborating remotely and in-person to empower our teams to work in the ways that work best for them.

A comprehensive benefits and compensation packagethat centers our teammates and helps them to bring their best to work every day:

Medical, Dental, and Vision Coverage

Holiday and Vacation Time

Health & Wellness Days

A Bonus Day for Your Birthday

 Learn more about the people behind our products athttps://www1.appliedsystems.com/en-us/about-us/jobs/

Our targeted starting base salary in the United States for this position ranges from $80,000 - $120,000. To determine a new team members starting pay, we consider a variety of factors, including someones depth, breadth, and variety of experience, skills, and responsibilities. Depending on the role, team members may also be eligible to participate in additional compensation plans such as bonus and commission.

Your Security Matters:Our candidates personal information and online safety are top of mind for us. At Applied, we proactively protect your personal information and only communicate with candidates via a secure @appliedsystems.com email or through our officialcareersportal. Recruiters will never request payments, ask for financial account information or sensitive information like social security numbers.

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