Lenovo Digital Trust Lab seeks a Security Infrastructure Developer to design and build the tools and platforms that empower our Hybrid AI Security and Device, Infrastructure and Services research teams. This role enables cutting-edge AI security research by providing scalable, cloud-native infrastructure, secure data pipelines, and automation frameworks.

You will act as a force multiplier for researchers, developing the tooling they need to test, prototype, and validate AI security controls—while ensuring that the underlying systems are secure, reliable, and production-ready.

You will play a hands-on role in developing cloud services, data pipelines, and monitoring systems that protect AI/ML assets at scale. As part of the Hybrid AI Security team, you will bridge the gap between applied research and production systems.

Job Responsibilities:

• Build and maintain internal security tooling for research teams (e.g., red-teaming automation, dataset validation tools, adversarial testing frameworks).
• Develop scalable cloud services and pipelines to support experimentation, prototyping, and model validation.
• Design secure data ingestion and processing flows while protecting sensitive datasets.
• Collaborate with researchers to translate prototypes into robust infrastructure components (monitoring, logging, dashboards, APIs).
• Integrate research tooling into CI/CD workflows to streamline testing and deployment.
• Evaluate and adapt emerging cloud and AI security technologies for internal use.

Minimum Requirements:

• Proven experience in developing and delivering cybersecurity products or platforms.
• B.Sc. or M.Sc. in Computer Science or related technical field.
• 5+ years of software development experience (Python, Go, or similar) with a focus on building developer-facing tools or platforms.
• Proven expertise in cloud platforms (AWS, Azure, or GCP) and their security services.
• Experience with data pipelines (Kafka, Spark, Airflow) and databases (SQL/NoSQL).
• Solid knowledge of containerization and orchestration (Docker, Kubernetes).
• Excellent problem-solving and collaboration skills, with the ability to support multiple research teams simultaneously.

Preferred Requirements:

• Experience with AI/ML pipelines and MLOps frameworks (Kubeflow, MLflow, SageMaker, Databricks).
• Hands-on work with infrastructure-as-code (Terraform, Helm, Ansible).
• Prior experience enabling R&D/research teams with tooling or platform development.