AI Security Engineer at Bright Vision Technologies

Frisco, Texas, United States -

Full Time

Start Date

Immediate

Expiry Date

15 Aug, 26

Salary

0.0

Posted On

17 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Yes

Skills

AI Security, LLM Security, Threat Modeling, Prompt Injection Defense, Python, Cloud Security, Application Security, Cryptography, Adversarial ML, Red Teaming, Incident Response, Identity and Access Management, Data Provenance, Security Architecture, Systems Programming, MLOps Security

Industry

Information Technology & Services

Description

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications. As we continue to grow, we’re looking for a skilled AI Security Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. AI Security Engineer Job Title: AI Security Engineer Location: 100% Remote (Continental United States) Position Type: In-house Bright Vision Technologies SOW engagement (no third-party client or vendor) Experience: 6+ years Sponsorship: No new H1B sponsorship available. H1B transfers welcomed for qualified candidates. Employment Type: Full-time, direct W2 with Bright Vision Technologies (no C2C, no 1099, no third-party) Engagement: Long-term, multi-year, aligned to the Bright Vision SOW delivery roadmap Compensation: Competitive base salary commensurate with experience, plus benefits. Employment Terms & Visa Policy This is a 100% remote, full-time, direct W2 position with Bright Vision Technologies. This role is part of Bright Vision Technologies’ in-house Statement of Work (SOW) engagement. The client, end customer, and employer for this position is Bright Vision Technologies — there is no third-party client, vendor, or implementation partner involved. We do not engage in C2C, 1099, or third-party arrangements for this role. BUT STRICTLY NO C2C/1099/3RD PARTY COMPANIES. ALL OUR ROLES ARE W2 AND NO 3RD PARTY BROKERING PLEASE. Candidates must be willing to work directly as a full-time W2 employee of Bright Vision Technologies and contribute to our in-house SOW deliverables. No new H1B sponsorship is available for this role. However, candidates who are currently on a valid H1B visa and require a transfer are welcome to apply. We will support H1B transfers for qualified candidates. For every role, a technical coding assessment is mandatory. Please apply only if you are confident in your technical abilities and hands-on experience. 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 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. Required Qualifications 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. Preferred Qualifications 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. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to harry@bvteck.com or contact us at (908) 676-4399. Learn more about Bright Vision Technologies at www.bvteck.com. We recognize that our people are our strength, and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Bright Vision Technologies is an Equal Opportunity Employer, including Disability/Veterans. Position offered by “No Fee Agency.” Equal Employment Opportunity (EEO) Statement Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall. BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.

Responsibilities

Lead the design and implementation of security controls, threat models, and incident response capabilities for AI and machine learning systems. Focus on mitigating risks such as prompt injection, model abuse, and data exfiltration in LLM-powered applications.