AI Infrastructure & Agent Systems Architect

Company Description Hawksbill is dedicated to harnessing Artificial Intelligence to create a more cyber secure and ethically driven future that prioritizes people. The organization focuses on building AI solutions that strengthen digital defenses while aligning with clear ethical standards. Team members collaborate across disciplines to ensure that technology decisions are transparent, responsible, and human-centered. Hawksbill offers opportunities to work on cutting-edge AI initiatives that have direct impact on cybersecurity and digital trust.

Role Description The AI Infrastructure & Agent Systems Architect is a full-time, on-site role based in Honolulu, HI (remote option available), responsible for designing, implementing, and maintaining robust AI infrastructure and agent-based systems. Day-to-day activities include designing and developing scalable compute and storage environments for AI workloads, defining system and integration architectures for agentic AI systems, and collaborating with engineering and data teams to ensure reliable deployment pipelines. The architect will continuously evaluate and optimize performance, security, and cost of AI platforms, document architectural standards and best practices, and guide technical decision-making for new AI initiatives. The role also involves hands-on prototyping, troubleshooting complex infrastructure issues, and working closely with stakeholders to align technical solutions with Hawksbill’s security and ethical AI objectives.

Qualifications

• Experience designing and operating AI/ML infrastructure, including cloud platforms (e.g., AWS, Azure, GCP), containerization (e.g., Docker), and orchestration tools (e.g., Kubernetes).
• Background in distributed systems, microservices architectures, and high-availability, scalable system design.
• Proficiency with infrastructure-as-code and automation tools (e.g., CI/CD pipelines) to ensure reproducible and reliable deployments.
• Understanding of AI agent frameworks, orchestration of multi-agent systems, and integration of LLMs or other AI models into production services.
• Strong knowledge of cybersecurity principles, secure architecture design, identity and access management, and data protection practices.
• Demonstrated ability to collaborate with software engineers, data scientists, and product teams, and to communicate complex technical concepts clearly.
• Bachelor’s or advanced degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent practical experience.
• Familiarity with ethical AI practices, model governance, and compliance considerations for AI systems is a plus.