Data and AI Security Architect

The Data and AI Security Architect at NXP will be tasked with securing the design, architecture, and implementation of innovative Data products and AI solutions aimed at enhancing NXP’s competitive hardware and software solutions. This role is pivotal in leveraging Data and AI technologies, security frameworks for AI (for example OWASP), and requires a deep understanding of Data Science, Statistics, Machine Learning, Generative AI, Large Language Models (LLMs), and Multimodal models. The Data and AI Security Architect will provide real-time insights and develop technology solutions that drive and sustain organizational performance and engagement.

Additionally, the Data and AI Security Architect will oversee the security architecture for AI solution teams, focusing on modifying existing products and creating new ones. The ideal candidate will be passionate about securing, designing, building, implementing, and maintaining industrial AI/ML/Generative AI applications. Leadership skills are essential to implement the latest AI techniques and security architectures and to continuously improve the AI/ML development, delivery, and operations process. The role involves adhering to best practices from Software Engineering, DevOps, MLOps, and LLMOps.

The Data and AI Security Architect will also be responsible for translating project requirements into strategic architecture solutions, ensuring the integration of cloud-native tools from major hyperscalers and machine learning to create chatbots, optimizations, and cognitive services. This role requires a blend of technical expertise and the ability to bridge the gap between intricate business challenges and transformative AI solutions, making it a strategically crucial position.

Job Responsibility:

Strategic Alignment and Vision

• Define and oversee the AI/ML/GenAI security direction and architectural vision, ensuring alignment with strategic goals and digital transformation efforts
• Translate the vision of business leaders into realistic security implementations, while identifying misaligned initiatives and impractical use cases

Architectural Design and Technology Selection

• Key contributor in architecting a comprehensive security AI Engineering framework that supports the deployment, evaluation, and management of ML models & GenAI solutions
• Select appropriate technologies from a pool of open-source and commercial offerings, considering deployment models and integration with existing tools
• Understand and contribute to MLOps and LLMOps, focusing on security capabilities and infrastructure to deploy and manage machine learning models and large language models

Collaboration and Stakeholder Engagement

• Collaborate with Enterprise, Application, Security, Data & DevOps Architects, Data scientists, Machine Learning & GenAI Engineers, and Business teams to pilot use cases and discuss architectural design
• Gather inputs from multiple stakeholders to align technical implementation with existing and future requirements
• Develop and maintain contact with top decision makers, legal, and other business units

Operational Excellence and Continuous Improvement

• Be responsible for the successful security architecture & design of AI-powered applications
• Audit AI tools and practices across data, models and software engineering, focusing on continuous improvement and feedback mechanisms
• Contribute to standardizing CI/CD pipelines, user and service roles, and container creation, model consumption, testing, and deployment methodology based on business and security requirements

Risk Management and Ethical Considerations

• Work closely with security and risk leaders to foresee and mitigate risks, ensuring ethical AI implementation and compliance with upcoming regulations
• Address potential issues such as training data poisoning, AI model theft, and adversarial samples

Product Strategy and Business Understanding

• Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products
• Break down client problems and bring an understanding of leading technology, analytics methods, tools, and operating model approaches

Research and Development

• Conduct research to provide security solutions to scale AI/ML powered features for real-world challenges, making trade-offs based on quality, scalability, performance, and cost

Tool Development and Data Management

• Build tools and capabilities that assist with data ingestion, feature engineering, data management, and organization
• Design, implement, and maintain distributed computing solutions for data processing and model training, ensuring the security, scalability, and reliability of machine learning infrastructure

Job Qualification:

Desired Education:

• Bachelor's Degree in any of Computer Science, Engineering, Statistics, Data Science or a related field
• Advanced degrees such as Masters is preferred

Professional Experience:

• 8 years of experience leading a cross-functional team (both onshore and offshore).
• 8 years of experience gathering non-functional requirements, performing designed and validated security architecture frameworks, and executing functional and testing assignments
• 2 years of experience building GenAI environments
• 8 years of experience translating security requirements into client ready design documents
• 8 years of experience with application architecture analysis, design, and delivery
• 8 years of full system development life cycle implementations

Soft Skills:

• Strong analytical and problem-solving abilities.
• Excellent communication and interpersonal skills for interacting with technical and non-technical stakeholders.
• Ability to manage multiple projects and priorities in a fast-paced environment.

Certifications (Preferred):

• Certifications in AI/ML technologies and Cloud platforms, such as AWS Certified Machine Learning – Specialty, Google Cloud Professional Machine Learning Engineer, Azure AI Engineer, Azure Data Scientist, or Azure Solutions Architect
• CISSP, CISM, TOGAF, or other security certifications