Staff - Security Architecture

We re looking for a Jr AI Security Architect to join our growing Security Architecture team. This role will support the design, implementation, and protection of AI/ML systems, models, and datasets. The ideal candidate is passionate about the intersection of artificial intelligence and cybersecurity, and eager to contribute to building secure-by-design AI systems that protect users, data, and business integrity. Key Responsibilities Secure AI Model Development - Partner with AI/ML teams to embed security into the model development lifecycle, including during data collection, model training, evaluation, and deployment. - Contribute to threat modeling exercises for AI/ML pipelines to identify risks such as model poisoning, data leakage, or adversarial input attacks. - Support the evaluation and implementation of model explainability, fairness, and accountability techniques to address security and compliance concerns. - Develop and train internal models for security purposes Model Training & Dataset Security - Help design controls to ensure the integrity and confidentiality of training datasets, including the use of differential privacy, data validation pipelines, and access controls. - Assist in implementing secure storage and version control practices for datasets and model artifacts. - Evaluate training environments for exposure to risks such as unauthorized data access, insecure third-party libraries, or compromised containers. AI Infrastructure Hardening - Work with infrastructure and MLOps teams to secure AI platforms (e.g., MLFlow, Kubeflow, SageMaker, Vertex AI) including compute resources, APIs, CI/CD pipelines, and model registries. - Contribute to security reviews of AI-related deployments in cloud and on-prem environments. - Assist in automating security checks in AI pipelines, such as scanning for secrets, validating container images, and enforcing secure permissions. Secure AI Integration in Products - Participate in the review and assessment of AI/ML models embedded into customer-facing products to ensure they comply with internal security and responsible AI guidelines. - Help develop misuse detection and monitoring strategies to identify model abuse (e.g., prompt injection, data extraction, hallucination exploitation). - Support product security teams in designing guardrails and sandboxing techniques for generative AI features (e.g., chatbots, image generators, copilots). Knowledge Sharing & Enablement - Assist in creating internal training and security guidance for data scientists, engineers, and developers on secure AI practices. - Help maintain documentation, runbooks, and security checklists specific to AI/ML workloads. - Stay current on emerging AI security threats, industry trends, and tools; contribute to internal knowledge sharing. Qualifications - 3-4 years of experience in LLM and 7-10 years of experience in cybersecurity, machine learning, or related fields. - Familiarity with ML frameworks (e.g., PyTorch, TensorFlow) and MLOps tools (e.g., MLFlow, Airflow, Kubernetes). - Familiarity with AI models and Supplychain risks - Understanding of common AI/ML security threats and mitigations (e.g., model inversion, adversarial examples, data poisoning). - Experience working with cloud environments (AWS, GCP, Azure) and securing workloads. - Some knowledge of responsible AI principles, privacy-preserving ML, or AI compliance frameworks is a plus. Soft Skills - Strong communication skills to collaborate across engineering, data science, and product teams. - A continuous learning mindset and willingness to grow in both AI and security domains. - Problem-solving approach with a focus on practical, scalable solutions.