Chief AI/ML Security Strategist

2 weeks ago

Melbourne, Victoria, Australia beBeeSecurity Full time $160,000 - $180,000
AI/ML Security Architect and Engineer

This is a critical role that requires designing, developing, and deploying AI-driven solutions to detect, prevent, and respond to cyber threats. Securing AI/ML systems, models, and data pipelines against adversarial threats is key.

Key Responsibilities:
  • Design and implement scalable generative AI features, including agentic workflows, conversational AI, and autonomous agents.
  • Develop and deploy machine learning models for threat detection, anomaly detection, malware classification, and behavioral analysis.
  • Apply best practices in AI security, including mitigation of hallucinations, prompt injection, and bias.
  • Identify and mitigate risks related to adversarial machine learning, model inversion, data poisoning, and prompt injection.
  • Analyze large-scale security datasets (e.g., logs, network traffic, endpoint telemetry) to identify patterns and build predictive models.
  • Research and implement AI and machine learning techniques to improve detection accuracy and reduce false positives.
  • Design and implement security controls for AI/ML systems, including model training, inference, and data pipelines.
  • Collaborate with security analysts and incident response teams to integrate AI tools into existing workflows.
  • Collaborate with data scientists, ML engineers, and DevOps teams to integrate security into the AI/ML lifecycle.
  • Build automation pipelines for data preprocessing, model training, evaluation, and deployment.
  • Monitor model performance and retrain models as needed to adapt to evolving threats.
Requirements:
  • Experience in full AI project lifecycle, from research and prototyping to deployment in production environments.
  • Familiarity with Agile development methodologies.
  • Proficiency in Python and ML libraries such as TensorFlow, PyTorch, Scikit-learn, or similar.
  • Experience with AI and/or data governance.
  • Experience with building automation solutions with AI/ML.
  • Knowledge of AI ethics, fairness, and explainability.
  • Strong understanding of cybersecurity principles, threat landscapes, and common attack vectors.
  • Experience with threat modeling and securing cloud-based AI infrastructure (e.g., AWS, Azure, GCP).
  • Experience with data engineering and working with large-scale datasets.

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