Secure AI Systems Engineer

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. They are seeking a Secure AI Systems Engineer to lead the design and implementation of security controls and threat models specifically tailored to AI and machine learning systems.

Responsibilities:

• Define and implement security controls specifically targeting LLM and AI-powered application risks
• Build threat models for AI systems, including prompt injection, jailbreaks, data exfiltration, and abuse patterns
• Design and deploy guardrails, content filters, and policy enforcement layers around model endpoints
• Implement runtime detection and response capabilities for adversarial prompts and abusive behavior
• Secure training and fine-tuning pipelines, including data provenance, integrity, and access controls
• Design controls for sensitive data handling, retention, and redaction in LLM workflows
• Lead red-team exercises against AI systems and drive remediation of identified weaknesses
• Evaluate and harden third-party AI services and open-source AI components used internally
• Implement identity, authorization, and tenant-isolation patterns for multi-tenant AI services
• Drive supply chain security for ML artifacts including weights, datasets, and inference dependencies
• Collaborate with privacy, legal, and compliance teams to ensure AI systems meet regulatory obligations
• Develop monitoring, logging, and detection strategies tailored to AI workloads
• Lead incident response for AI-specific security events and drive durable improvements
• Stay current with adversarial ML, LLM security research, and emerging regulatory developments

Requirements:

• Bachelor's or Master's degree in Computer Science, Cybersecurity, or a related discipline
• Six or more years of security engineering experience, including significant work on AI or ML systems
• Strong understanding of LLM internals, modern AI architectures, and common failure modes
• Hands-on experience designing security controls for AI-powered applications
• Deep knowledge of application security, identity, and cryptography fundamentals
• Experience with threat modeling and security architecture review processes
• Familiarity with adversarial ML, prompt injection, and model abuse research
• Proficiency in Python and at least one systems language
• Strong understanding of cloud security and modern infrastructure controls
• Excellent written and verbal communication skills
• Publications, talks, or CTF participation in AI security topics
• Experience with red-teaming LLM-based products
• Familiarity with privacy-preserving ML techniques such as differential privacy
• Exposure to regulated industries with strict data handling requirements
• Open-source contributions to AI security tooling