Machine Learning Security Specialist

Key Responsibilities

• Design and implement end-to-end machine learning pipelines for AI cybersecurity applications.
• Deploy and manage machine learning models in production using tools like MLflow, Kubeflow or AWS SageMaker.
• Build and maintain dashboards using Grafana, Prometheus or Kibana to track real-time model health and historical trends.
• Implement drift detection pipelines using tools like Evidently AI or Alibi Detect to identify shifts in data distributions and trigger alerts or retraining.
• Set up centralized logging with ELK Stack or OpenTelemetry to capture AI inference events, errors and audit trails for debugging and compliance.
• Develop CI/CD pipelines with GitHub Actions or Jenkins to automate model updates, testing and deployment.
• Apply secure-by-design principles to protect data pipelines and models, using encryption, access controls and compliance with regulations like GDPR or NIST AI RMF.
• Collaborate with data scientists, AI Integration Engineers and DevOps teams to align model performance with business requirements and infrastructure capabilities.
• Optimize models for production via quantization or pruning and ensure efficient resource usage on cloud platforms like AWS, Azure or Google Cloud.

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