• Define a broad enterprise PKI strategy and reach alignment across the organization.
• Design the security architecture for a state-of-the-art and future-proof PKI infrastructure, with adequate Certificate Authority (CA) topology, PKI technology, certificate lifecycle management tools and other components.
• Lead the strategic transition of the PKI infrastructure to Post-Quantum Cryptography (PQC), ensuring that cryptographic systems are designed with cryptographic agility in mind.
• Closely collaborate with the IT and Operations teams in charge of deploying and operating the PKI infrastructure.
• Oversee critical PKI processes such as root key ceremonies and other essential CA processes to maintain the integrity and trust of our PKI systems. Develop and maintain detailed documentation of PKI processes.
• Ensure proper governance framework, certificate policies and practices statement are implemented and maintained.
• Define and implement robust and reliable incident, response, and recovery procedures. 
• Ensure compliance with relevant regulations and industry standards. Support internal and external audit reviews.
• Act as PKI centre of expertise, providing specialized knowledge and guidance on all aspects of PKI systems.
• Keep abreast with the latest trends, technologies, and industry best practices and serve as a key advisor.

What you need to be successful:

• University degree in Computer Science, AI/ML, Data Science or related field, or equivalent experience. Cybersecurity background is a plus. 
• 8-10 years of relevant experience, including in AI/ML models development and deployment.
• Proficiency in programming languages such as Python, Java, or C++, and in AI/ML frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, Keras, and XGBoost.
• Strong understanding of security concepts, including data privacy controls, secure coding practices, threat modeling, and risk assessment.
• High interest to learn and apply cyber security to AI/ML systems, including protecting against adversarial ML attacks, ensuring the integrity of model training and inference processes and the confidentiality of model and trained data.
• Strong analytical and problem-solving skills, attention to detail, and ability to work in a collaborative team environment.
• Excellent communication skills, including the ability to translate complex technical information for a non-technical audience.