Sr. Security Solutions Architect, AI-Applied Guidance, Well-Architected Solutions Innovation

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset.

Do you want to set the standard for how builders architect AI workloads that are secure, reliable, and efficient on AWS with security as the foundation? We are looking for a Senior Security Solutions Architect with deep expertise in Machine Learning, Generative AI, and Agentic AI to own and drive the strategic vision for security-focused architectural guidance and best practices across AI workloads. You will operate at the intersection of AI technologies cloud architecture and security engineering ensuring AI workloads achieve Well-Architected outcomes across all six pillars of Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability with depth in the Security pillar. You will influence both internal AWS teams and the broader builder community. In this role you will define the long-term technical direction for AI security architectural guidance translate emerging AI/ML security patterns into prescriptive and actionable best practices grounded in the Well-Architected pillars and influence AWS service design to better serve AI builders who need to secure their workloads across the full AI lifecycle.

Key job responsibilities • Set Strategic Technical Direction for AI Security: Define and own the long-term vision and roadmap for AI/ML security architectural guidance aligned to the AWS Well-Architected Guidance pillars with deep focus on the Security pillar - including identity, access control, data protection, data residency and sovereignty, threat detection, and incident response for AI workloads. • Provide guidance on security considerations for foundation models, RAG pipelines, fine-tuning, multi-agent orchestration, and responsible AI practices. Drive consensus on complex ambiguous security decisions in AI systems. • Raise the Bar Across the Organization: Establish security standards review mechanisms and architectural guardrails that elevate the entire guidance portfolio. Define what "great" looks like for AI security guidance and hold the team accountable. • Influence Service Roadmaps: Partner with Stakeholders and Engineers across AWS service teams (Amazon Bedrock, SageMaker, Q, AWS Security services, etc.) to represent the customer voice on AI security validate architectural recommendations and influence product direction based on security patterns observed in production AI workloads. • Executive Customer Engagement: Engage directly with strategic enterprise customers to validate security guidance through real-world implementations identify emerging AI security challenges and translate insights into scalable best practices. • Drive Innovation in Content Delivery: Own the strategy for automation tooling and pipelines (including Generative AI-assisted authoring) that accelerate the creation review and publication of AI security guidance at scale. Define mechanisms that ensure guidance remains current as the AI threat landscape evolves rapidly. • External Thought Leadership: Publish whitepapers blog posts and reference architectures; present at AWS events (re:Invent, Summits, webinars) and industry security conferences to establish AWS as a leading authority on securing Well-Architected AI workloads. • Mentorship & Organizational Impact: Mentor senior architects on AI security best practices drive hiring bar-raising and build a community of practice that scales AI security architectural expertise across the organization. Contribute to organizational strategy and workforce planning. • Mechanisms & Operational Excellence: Design and implement repeatable mechanisms (e.g., security review processes, threat modeling for AI workloads, guidance lifecycle management, feedback loops) that ensure sustained quality and relevance at scale. • Hands-On Technical Validation: Validate security architectural recommendations through prototyping proof-of-concept implementations threat modeling exercises and code samples that demonstrate secure AI patterns.

A day in the life You start your morning reviewing a customer's architecture proposal identifying areas where you can simplify complexity and reduce operational overhead. By midday you are leading a technical deep-dive with a customer's engineering team walking through design choices and trade-offs. Later you collaborate with internal partners to unblock a delivery milestone then wrap up by contributing a reusable reference design that other architects can leverage.

About the team Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying.

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We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. - Bachelor's degree in computer science, engineering, mathematics or equivalent - 7+ years of experience in solutions architecture, security architecture, or a senior technical role with increasing scope and impact - 3+ years of hands-on experience in cloud security, application security, or infrastructure security - Deep understanding of the end-to-end ML lifecycle from a security perspective: secure data preparation, model integrity, supply chain security, deployment hardening, monitoring, and governance - Demonstrated expertise in security domains: identity and access management, encryption, network security, threat modeling, vulnerability management, and compliance - Demonstrated ability to influence technical direction across organizational boundaries without direct authority - Experience with AWS security and AI/ML services (IAM, KMS, GuardDuty, Security Hub, Bedrock, SageMaker, etc.) - Knowledge of AWS services, market segments, customer base and industry verticals - Deep expertise in securing Generative AI architectures: model access controls, data leakage prevention, prompt injection defenses, RAG pipeline security, vector database access patterns, and secure fine-tuning workflows - Hands-on experience securing Agentic AI patterns: permission boundaries for autonomous agents, secure tool-use architectures, least-privilege orchestration, audit trails, and human-in-the-loop approval gates - Expert-level kn