Presales Solutions Architect - Security

About the role

The Presales Solutions Architect - AI Security at SHI International is responsible for being SHI's Subject Matter Expert on all aspects of AI and machine learning security.

Responsibilities

• Design and deliver product demonstrations, proof-of-concepts (POCs), and technical presentations focused on AI security solutions including AI threat modeling tools, ML pipeline security platforms, model scanning, and adversarial testing frameworks.
• Lead technical discovery sessions to assess clients' AI security maturity across model protection, data governance, inference security, and AI-specific incident response capabilities.
• Translate complex AI security concepts – such as adversarial machine learning, prompt injection defense, model poisoning mitigation, and MITRE ATLAS mapping – into business-relevant value propositions for both technical and executive audiences.
• Design and present target-state AI security architectures covering secure model training pipelines, inference endpoint protection, data provenance controls, model registries, and integration with broader security stacks (CNAPP, IAM, zero trust).
• Evaluate and qualify new AI security vendor technologies and product capabilities; make recommendations on partner adoption, platform consolidation, and legacy solution retirement.
• Conduct internal enablement sessions for engineering and sales teams on emerging AI threats, product updates, use cases, and AI security best practices.
• Drive revenue growth within the presales team by identifying new AI security opportunities and optimizing service offerings.
• Collaborate with SHI stakeholder partner teams to create synergistic AI security service solutions that integrate across SHI’s broader portfolio.
• Build and maintain strong relationships with key clients and partners, ensuring high levels of satisfaction and retention through product briefings, demonstrations, and knowledge transfer.
• Stay current with new AI security technologies, frameworks, and regulatory developments; track adversary TTPs targeting AI/ML systems and the evolving threat landscape to inform architectural recommendations.
• Contribute to practice development by identifying areas for growth, leading innovation initiatives, and creating and maintaining practice standards to ensure high-quality service delivery.

Qualifications

• Completed Bachelor’s degree in Computer Science, Cybersecurity, Data Science, Artificial Intelligence, or equivalent work experience.
• At least 5+ years of experience in cybersecurity, with at least 1–2 years focused on AI/ML security, cloud security, or application security in roles involving hands-on architecture design and implementation.
• Demonstrated experience in presales, solutions architecture, or technical consulting within the cybersecurity or AI security domain.
• Willingness to travel occasionally up to 20%.

Skills

• Proficiency in overseeing and directing projects to completion, ensuring goals are met, resources are utilized efficiently, and stakeholders are satisfied.
• Ability to systematically identify, document, and manage the technical needs and specifications of a project by engaging with stakeholders and analyzing business objectives to ensure successful project outcomes.
• Ability to define, design, build, and maintain robust AI security architectures and solutions by leveraging enterprise security platforms, AI/ML tooling, and cloud infrastructure effectively.
• Familiarity with programming languages (Python, Go, or Bash) and their application to security automation, AI model scanning, adversarial testing, and integration workflows.
• Ability to understand and manage various infrastructure components (firewalls, load balancers, hypervisors, storage, monitoring, security) and use orchestration tools to develop comprehensive technical solutions.
• Ability to develop detailed opportunity engagement scoping documents that accurately define deliverables and requirements.
• Deep understanding of the AI development lifecycle security including secure data collection, model training isolation, inference endpoint hardening, AI-specific incident response, and continuous improvement frameworks.