AI Systems Strategist

2 days ago

Bengaluru, Karnataka, India beBeeMachineLearning Full time US$ 2,00,000 - US$ 2,50,000

At the forefront of AI and ML innovation, we're seeking a visionary leader to drive strategic growth and development. Your expertise will be pivotal in shaping our approach to AI systems, ensuring their safety, security, and effectiveness.

About the Role
  • Threat Simulation: Develop and execute red teaming exercises to identify vulnerabilities in AI systems, driving improvements in adversarial testing and model robustness.
  • Cross-Functional Collaboration: Collaborate with AI/ML engineering, enterprise cybersecurity, product, legal, and compliance teams to embed red teaming insights into model development and deployment lifecycles.
  • Governance and Compliance: Ensure alignment with internal policies for responsible AI use, upholding the highest standards of ethics and accountability.
  • Innovation and Research: Stay at the cutting edge of adversarial ML, model exploitation techniques, and AI safety research, fostering a culture of continuous learning and innovation.
  • Incident Response: Lead investigations into AI-related security incidents, developing mitigation strategies and post-mortem analyses.

Requirements

  • An advanced degree (MS or PhD) in Computer Science, Machine Learning, Cybersecurity, or a related field.
  • 8+ years of experience in Machine Learning.
  • 4+ years of experience in cybersecurity, red teaming, or adversarial testing.
  • 3+ years of experience with generative AI systems (e.g., LLMs, agents) and their unique threat surfaces.

Benefits

  • A dynamic and supportive work environment that encourages collaboration and knowledge-sharing.
  • Opportunities for professional growth and development in a rapidly evolving field.
  • The chance to make a meaningful impact on the future of AI and ML.

Preferred Qualifications

  • Experience working in regulated industries (e.g., finance, healthcare, defense) with AI governance requirements.
  • Knowledge of secure model deployment practices in cloud and edge environments.
  • Familiarity with AI safety, fairness, and interpretability frameworks.
  • Demonstrated contributions to open-source tools, academic research, or AI security communities.

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