Senior Security Solutions Architect, Ai-driven Guidance, Well-architected Solutions Innovation

Amazon Amazon · Big Tech · Bellevue, WA · Solutions Architect

Senior Security Solutions Architect focused on AI-driven guidance for secure, reliable, and efficient AI workloads on AWS. The role involves defining strategic technical direction, providing thought leadership on AI security, influencing service roadmaps, engaging with enterprise customers, and driving innovation in content delivery and automation for AI security best practices.

What you'd actually do

  1. 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.
  2. AI Security & Generative AI Thought Leadership: Serve as the organization's principal technical authority on securing AI architectures — covering model security, data pipeline protection, prompt injection mitigation, model poisoning prevention, inference endpoint hardening, and secure agentic workflows. 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.
  3. 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.
  4. 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.
  5. 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.

Skills

Required

  • Deep expertise in Machine Learning
  • Generative AI
  • Agentic AI
  • Cloud architecture
  • Security engineering
  • AWS Well-Architected Framework
  • Identity and access control
  • Data protection
  • Data residency and sovereignty
  • Threat detection
  • Incident response
  • Model security
  • Data pipeline protection
  • Prompt injection mitigation
  • Model poisoning prevention
  • Inference endpoint hardening
  • Secure agentic workflows
  • Foundation models
  • RAG pipelines
  • Fine-tuning
  • Multi-agent orchestration
  • Responsible AI practices
  • Security standards
  • Review mechanisms
  • Architectural guardrails
  • AWS service teams (Amazon Bedrock, SageMaker, Q, AWS Security services)
  • Enterprise customer engagement
  • Prototyping
  • Proof-of-concept implementations
  • Threat modeling
  • Code samples

Nice to have

  • 8+ years of specific technology domain areas (e.g. software dev

What the JD emphasized

  • security as the foundation
  • AI workloads
  • AI lifecycle
  • AI Security
  • Generative AI
  • Agentic AI
  • AI architectures
  • AI security
  • AI workloads
  • AI security guidance
  • AI security challenges
  • AI security architectural expertise
  • AI workloads
  • AI security
  • AI security best practices

Other signals

  • AI Security
  • Generative AI
  • Agentic AI
  • Well-Architected Framework
  • Cloud Architecture