Principal Enterprise AI Systems Architect, Core42 | Abu Dhabi, UAE

About Us

Core42 delivers AI-powered cloud and digital infrastructure, enabling organizations to harness sovereign AI capabilities and scalable, high-performance computing. Through advanced technology and strategic partnerships, Core42 supports digital transformation across regulated industries and global markets.

The Opportunity

As Principal Enterprise AI Systems Architect, you will serve as the end-to-end technical authority for complex enterprise AI platforms spanning applications, data, AI models, cloud, accelerated computing, networking, storage, security and operations. You will translate business objectives and workload requirements into secure, scalable, resilient and production-ready architectures. This is a hands-on architecture and engineering leadership role, covering discovery, design, validation, implementation readiness and production adoption—not a conventional presales or proposal-management position.

You will work with customers, engineering teams, Product Management, Technical Program Managers and strategic technology partners across the UAE and international markets. You will also translate recurring customer needs into reusable Core42 architectures and platform capabilities supporting Compass, sovereign AI, enterprise LLMs, agentic workloads and HPC services.

Key Responsibilities

• Enterprise Architecture and Discovery: Lead technical discovery with customers, translating business objectives, application and data landscapes, workload requirements, performance expectations, security constraints and sovereignty requirements into end-to-end architectures.
• Strategic Pursuits and Partnerships: Serve as the senior technical architecture authority for strategic engagements. Lead architecture reviews, technical due diligence and solution validation, ensuring customer standards, assumptions and deviations are clearly documented and deliverable.
• End-to-End Platform Architecture: Design integrated architectures spanning applications, APIs, distributed systems, data platforms, AI/LLM services, MLOps/LLMOps, cloud, compute, networking, storage, identity, security and operations.
• AI and Data Architecture: Architect enterprise AI solutions covering LLMs, RAG, enterprise search, agentic workflows, model serving, evaluation, guardrails, responsible AI, data governance, vector retrieval and batch or real-time data processing.
• Infrastructure, HPC and Accelerated Computing: Design GPU and AI accelerator platforms, clusters, high-performance networking, parallel storage, workload orchestration, schedulers, distributed training and inference, capacity planning and performance optimization.
• Cloud, Hybrid and Sovereign Platforms: Design public, private, hybrid, on-premises, edge and restricted environments. Address landing zones, tenancy, connectivity, interoperability, data residency, jurisdiction, encryption and key custody, privileged access, control-plane location and operational autonomy.
• Security, Resilience and Operations: Embed zero-trust security, identity and access management, encryption, threat modelling, observability, disaster recovery, business continuity, incident response, lifecycle management and operational supportability into architecture decisions.
• Architecture Validation and Delivery Assurance: Lead prototypes, technical spikes, benchmarks and architecture reviews to validate assumptions and retire risks. Produce implementation-ready architecture documentation and maintain design integrity through deployment and production acceptance.
• Performance and Economics: Evaluate architectural options against performance, scalability, availability, security, sovereignty, supportability, time to value and total cost of ownership.
• Product and Platform Alignment: Partner with Product Management to provide technical feasibility assessments, platform constraints, architecture options and customer insights that inform product requirements, roadmap decisions, release readiness and lifecycle evolution. Assess build, buy, partner and integration options without assuming product ownership.
• Pursuit-to-Production Continuity: Maintain requirements traceability and alignment across discovery, solution design, technical and contractual assumptions, implementation readiness and operational handover. Identify technical, commercial-assumption and delivery risks early.
• Architecture Governance and Leadership: Lead architecture workstreams, manage technical decisions, dependencies and risks in partnership with Technical Program Managers, mentor technical teams and develop reusable reference architectures, standards, design patterns and deployment guides.

Required Skills and Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Engineering, Information Systems or a related field, or equivalent practical experience.
• 10+ years of experience in enterprise, software, cloud, platform or infrastructure architecture, or senior engineering roles supporting complex production systems.
• Proven ability to architect integrated solutions across software, data, AI and infrastructure rather than operating within a single technology domain.
• Strong software architecture expertise covering distributed systems, APIs, integration, containers, Kubernetes, CI/CD, data platforms, observability and production operations.
• Strong infrastructure expertise covering compute, GPU-accelerated platforms, virtualization or bare metal, networking, storage, cluster architecture, performance, resilience and capacity planning.
• Practical experience with enterprise AI and LLM architectures, RAG, model serving, inference, agentic workflows, evaluation and MLOps/LLMOps.
• Experience designing HPC or distributed AI platforms, including workload orchestration, high-performance networking, scale-out storage, cluster management and performance engineering.
• Experience with public, private and hybrid cloud architectures, including governance, identity, tenancy, security, resilience, interoperability and cost management.
• Ability to translate approved regulatory, security and sovereignty requirements into architecture decisions in collaboration with legal, risk, compliance and security stakeholders.
• Experience validating architectures through prototypes, benchmarks or technical testing, and producing clear architecture diagrams, decision records, integration designs, requirements traceability and implementation guidance.
• Proven ability to support strategic customer engagements, technical due diligence, architecture alignment and transition from solution design into delivery and operations.
• Excellent technical communication, stakeholder management, analytical thinking and leadership skills, with the ability to influence executives, customers, engineering teams and technology partners.

Preferred Qualifications

• Master's degree in Computer Science, Engineering, Artificial Intelligence or a related field.
• Experience with sovereign AI platforms, AI factories, large-scale inference services or GPU-accelerated application and data platforms.
• Experience with major accelerated-computing ecosystems, AI accelerators, model-serving stacks, distributed training and inference, and high-performance fabrics.
• Familiarity with Microsoft Azure and Azure AI services, AWS or Google Cloud, and hybrid-cloud interoperability.
• Experience with enterprise data and analytics platforms, vector databases, streaming, data governance and retrieval architectures.
• Experience in regulated or mission-critical sectors, including government, healthcare, financial services, energy or defense, and in sovereign, disconnected or air-gapped environments.
• Working knowledge of data-centre power, cooling, rack density and physical integration; relevant architecture, cloud, security, AI or infrastructure certifications; and UAE or wider Middle East delivery experience.

What Success Looks Like

• Integrated architectures meet business, performance, security, sovereignty and operational requirements across the complete technology stack.
• Requirements and architecture decisions remain traceable from discovery through production acceptance.
• Technical and delivery risks are identified and validated early, reducing ambiguity, rework and implementation delays.
• Customers and partners have confidence in the feasibility, scalability and operational readiness of Core42 solutions.
• Reusable architecture patterns improve delivery consistency and inform Core42 product capabilities and platform evolution.

Role Scope and Boundaries

This role owns the technical architecture workstream and end-to-end platform design. It works alongside Forward Deployed Engineers, who build and deploy customer-facing applications and services, and Technical Program Managers, who retain overall program governance.

The role provides technical evidence and feasibility input to Product Management but does not own product strategy or roadmap prioritization. It supports strategic pursuits through architecture leadership but does not own sales quotas or pipeline management. Legal, risk, compliance and security stakeholders retain accountability for policy interpretation, approvals and control ownership.