Job description

Senior Security Engineer, Artificial Intelligence Position Summary We are seeking a Senior Security Engineer, Artificial Intelligence to help secure AI-powered products, platforms, and services across the full development lifecycle. This role will be responsible for designing, implementing, and maintaining security controls for AI and machine learning environments, while partnering closely with Engineering, Product, Data Science, Privacy, and Compliance teams. The ideal candidate will have a strong background in security engineering, cloud security, and AI security, with experience protecting LLM-based applications, machine learning pipelines, and AI-enabled business solutions. Key Responsibilities Secure AI & Machine Learning Systems Define and implement security requirements for AI and ML development, training, deployment, and monitoring. Conduct threat modeling exercises for AI-enabled products and services. Design secure patterns for LLM applications, RAG architectures, AI agents, and model APIs. Implement controls for model access, authorization, secrets management, and tenant isolation. Protect AI Data Assets Establish controls for training data, fine-tuning datasets, prompts, embeddings, and generated outputs. Implement safeguards for sensitive data, including PII, financial, and regulated information. Define requirements for data retention, deletion, lineage, and governance. Partner with Privacy and Legal teams on responsible AI and data protection initiatives. Secure AI Infrastructure & Supply Chain Secure cloud-based AI platforms, model registries, containers, and deployment pipelines. Assess risks associated with third-party models, libraries, frameworks, and AI vendors. Implement controls for model integrity, artifact validation, and supply chain security. Build automated security checks into CI/CD and MLOps workflows. AI Threat Detection & Incident Response Develop monitoring, logging, and alerting standards for AI platforms and services. Build detections for prompt injection, data leakage, model abuse, adversarial attacks, and abnormal usage patterns. Create response playbooks for AI-related security incidents. Conduct security assessments, penetration testing, red teaming, and adversarial testing exercises. Governance & Security Leadership Develop AI security standards, reference architectures, and security best practices. Lead security reviews for new AI initiatives and vendor assessments. Establish metrics and KPIs to measure AI security effectiveness and maturity. Educate engineering and product teams on secure AI development practices. Technologies & Environments Experience with some or all of the following: Cloud Platforms: AWS, Azure, GCP Containers & Orchestration: Kubernetes, Docker DevSecOps & Infrastructure as Code: Terraform, GitHub Actions, GitLab CI, Azure DevOps Security Tools: SIEM, EDR, WAF, API Security Platforms, Secrets Management Solutions AI/ML Technologies: Large Language Models (LLMs), RAG Architectures, Agentic Systems, Model Registries, Vector Databases, ML Pipelines Required Qualifications Top Must-Haves 3+ years of experience securing AI/ML, LLM, RAG, or AI-enabled applications Hands-on experience with cloud security (AWS, Azure, or GCP) Experience designing and implementing security controls for modern application, API, or AI environments Additional Requirements 5+ years of experience in Security Engineering, Application Security, Cloud Security, Detection Engineering, or related cybersecurity disciplines. Experience securing LLM-powered applications, retrieval-augmented generation (RAG) systems, or AI agent workflows. Understanding of AI security threats including prompt injection, model extraction, model inversion, adversarial attacks, and data poisoning. Hands-on experience with Python or another modern programming language. Strong knowledge of API security, identity and access management, secrets management, and cloud security best practices. Experience integrating security controls into CI/CD pipelines. Ability to communicate effectively with both technical and business stakeholders. Proven ability to lead cross-functional security initiatives and influence security best practices across organizations. Preferred Qualifications Bachelor's degree in Cybersecurity, Computer Science, Artificial Intelligence, Information Systems, or related technical field. Master's degree in a related discipline. Experience with Kubernetes security and container security platforms. Experience developing detection use cases within SIEM or EDR technologies. AI or cybersecurity certifications, including: CISSP GIAC certifications AWS Machine Learning Specialty Microsoft Azure AI Engineer Certified Cloud Security Professional (CCSP)

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aimachine learningllmmlci/cdawsazuregcpkubernetesdockerapipython

Platform: careers-page
Effort: medium