$ cat jobs/ml-security-engineer-bright-vision-technologies-89f8fb447d85.json
ML Security Engineer
ML Security Engineer- Remote Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. Job Title: ML Security Engineer Location: 100% Remote (U.S.) Position Type: Full-time, Direct W2 Salary Range: $100,000–$150,000 Annually Experience Required: 6+ years Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position. Job Summary We are seeking an AI Security Engineer to lead the design and implementation of security controls, threat models, and incident response capabilities specifically tailored to AI and machine learning systems. The role addresses the unique security challenges posed by LLMs, model APIs, training data pipelines, and AI-powered applications, including prompt injection, model abuse, data exfiltration, and supply chain risks. The ideal candidate has strong security engineering fundamentals and a deep understanding of how modern AI systems work in practice, with hands-on experience designing defenses for both AI-powered applications and the AI infrastructure that supports them. Key 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
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