About the job
Embrace the opportunity to become a Product Security Engineer and help build AI-powered security platforms at Adobe. Leverage LLMs, threat modeling, and cloud technologies to secure innovative products. Collaborate with security and engineering teams, drive risk-based decisions, and shape the future of secure software. Grow your career with cutting-edge technology and impactful projects.
Responsibilities
Build security analysis capabilities using LLM integrations with Azure OpenAI, prompt engineering, retrieval-augmented generation, and vector-based context retrieval.
Develop and maintain platforms end to end using React, Python FastAPI, Celery, Postgres, Redis, and Kubernetes with Argo.
Evaluate LLM and retrieval outputs to ensure accuracy and reliability for internal users.
Address AI-specific risks like prompt injection, data exposure, and output manipulation.
Make architecture decisions for new security capabilities, balancing performance, scalability, maintainability, and responsible AI use.
Identify security gaps with internal users and propose features to close them.
Partner with security and product teams to translate their needs into scalable features.
Use AI-assisted tools like GitHub Copilot and Cursor to move faster without compromising code quality.
Share knowledge with peers, support teammates, and contribute to Adobe’s security community.
Qualifications
Minimum
Bachelor’s degree or equivalent practical experience in Computer Science, Engineering, or a related field.
4 to 6 years of software development experience, including full-stack or backend systems.
Solid foundation in Secure SDLC practices, application security, and threat modeling.
Proficiency in Python and JavaScript, with React experience preferred.
Experience with AI systems, including LLMs, prompt engineering, AI APIs like Azure OpenAI, and vector databases or retrieval-based systems.
Understanding of AI-specific risks like prompt injection and hallucination, with experience evaluating AI outputs.
Familiarity with cloud platforms, preferably Azure, and containerized deployments.
Knowledge of CI/CD pipelines, Git, and modern development workflows.
Ability to work independently and across teams, with judgment to make risk-based decisions and communicate technical risk clearly.
Preferred
No preferred qualifications listed.