Principal Enterprise AI Systems Architect

About Core42

Opportunity

Architecture and AI platforms

• Leads customer discovery, architecture reviews, technical due diligence, solution validation, and design of integrated enterprise platforms.
• Architects AI solutions involving LLMs, RAG, enterprise search, agents, model serving, evaluation, guardrails, responsible AI, data governance, and data processing.
• Designs GPU and AI accelerator platforms, HPC clusters, high-performance networking, storage, orchestration, distributed training and inference, and performance plans.
• Designs public, private, hybrid, on-premises, edge, and restricted environments, including sovereignty, residency, encryption, and operational autonomy.

Security, validation and delivery

• Embeds zero-trust security, identity, encryption, threat modeling, observability, disaster recovery, continuity, incident response, and operational supportability.
• Leads prototypes, technical spikes, benchmarks, and architecture reviews; produces implementation-ready documentation and maintains design integrity through production acceptance.
• Evaluates options for performance, scalability, availability, security, sovereignty, supportability, time to value, and total cost of ownership.
• Maintains requirements traceability and supports continuity from discovery and design through implementation readiness and operational handover.

Leadership and product alignment

• Advises Product Management on feasibility, constraints, architecture choices, customer insights, release readiness, and platform evolution without owning product strategy.
• Leads architecture workstreams and technical decisions, mentors teams, manages dependencies and risks, and develops reusable architecture patterns and deployment guides.
• The role owns the technical architecture workstream, but not overall program governance, product roadmap prioritization, sales quotas, or policy approvals.

Required qualifications

• Bachelor’s degree in computer science, software engineering, engineering, information systems, or a related field, or equivalent practical experience.
• 10+ years in enterprise, software, cloud, platform, or infrastructure architecture, or senior engineering roles supporting complex production systems.
• Experience integrating software, data, AI, and infrastructure architectures; expertise in distributed systems, APIs, containers, Kubernetes, CI/CD, data platforms, observability, and operations.
• Infrastructure experience across compute, GPU platforms, networking, storage, clusters, performance, resilience, and capacity planning; practical enterprise AI and LLM architecture experience.
• Experience in HPC or distributed AI platforms, public/private/hybrid cloud, architecture validation, technical documentation, and strategic customer engagements.
• Ability to translate approved regulatory, security, and sovereignty requirements into architecture decisions; strong communication, stakeholder, analytical, and leadership skills.

Preferred qualifications

• A master’s degree in computer science, engineering, artificial intelligence, or a related field is preferred.
• Preferred experience includes sovereign AI platforms, AI factories, large-scale inference, accelerated-computing ecosystems, Azure, AWS, Google Cloud, hybrid-cloud interoperability, vector databases, streaming, and data governance.
• Regulated or mission-critical sector experience and sovereign, disconnected, or air-gapped environments are preferred.
• Working knowledge of data-center power, cooling, rack density, or physical integration; relevant architecture, cloud, security, AI, or infrastructure certifications; and UAE or Middle East delivery experience are preferred.

Success measures and role boundaries