Senior Solution Architect

Senior Solution Architect Location: Abu Dhabi, UAE Inception42, a G42 company, is the region’s leading innovator of AI-powered domain-specific as well as industry-agnostic products, built on a rich heritage of research and development. Within the G42 ecosystem, Inception42 functions as the core intelligence layer – transforming data and compute infrastructure into real-world, applied AI solutions. Beyond its commercial endeavors, Inception42 is committed to creating positive societal impact. For more information, please visit www.inceptionai.ai Overview You will play a key role in shaping and delivering enterprise-grade AI, data, and digital solutions across cloud, hybrid, and sovereign environments. The role turns complex business and technical requirements into secure, scalable, and production-ready architectures that engineering teams can execute. You will work closely with product, engineering, data science, security, delivery, and commercial teams from discovery through deployment. This is a hands-on architecture role for someone who can make clear technical trade-offs, resolve cross-system dependencies, and maintain architectural coherence while delivery moves at pace. What You’ll Own • Define the architectural vision and target state for enterprise AI, data, and digital solutions. • Translate business, product, and operational requirements into end-to-end solution designs, reference architectures, and implementation guidance. • Design secure, resilient, observable, and scalable systems across public cloud, private cloud, hybrid, and sovereign environments. • Evaluate build-versus-buy decisions and technology options using clear criteria for value, risk, operability, portability, and long-term ownership. • Shape architectures for AI and generative AI workloads, including model services, retrieval-augmented generation, data pipelines, evaluation, monitoring, and lifecycle controls. • Define integration patterns for APIs, microservices, event-driven systems, data-intensive applications, identity, and enterprise platforms. • Identify opportunities to reuse platform capabilities, architecture patterns, and shared services across products and deployments. • Drive architecture reviews and technical decisions, documenting trade-offs and resolving dependencies across teams and systems. • Partner with security and governance teams to embed privacy, data sovereignty, access control, web security, auditability, and responsible AI requirements by design. • Support discovery, solution shaping, technical proposals, proof-of-value work, estimation, and delivery planning with credible architecture and risk assumptions. • Stay close to implementation, validating that delivered systems remain aligned with the intended architecture and production requirements. • Raise the technical bar through mentoring, practical design feedback, and reusable standards for engineers and solution teams. What We’re Looking For • Strong software and systems engineering fundamentals, with the ability to reason across application, data, infrastructure, security, and operational concerns. • Experience designing and shipping production systems in complex enterprise environments. • Practical depth in at least one major cloud platform and sound judgment across cloud-native, hybrid, and constrained deployment models. • Working knowledge of APIs, microservices, containers, Kubernetes, infrastructure as code, distributed systems, and modern data architecture. • Ability to engage credibly with AI and machine learning teams and design the platform, data, integration, evaluation, and governance layers around model capabilities. • Strong architecture communication: clear diagrams, concise decision records, implementation-ready guidance, and effective technical presentations. • A track record of navigating ambiguity, surfacing risk early, and making pragmatic trade-offs without losing sight of long-term system quality. • High ownership and strong collaboration across product, engineering, security, delivery, and business stakeholders. Nice to Have • Experience with domain-driven design, CQRS, event sourcing, or other patterns for complex distributed applications. • Exposure to LLM platforms, RAG systems, LLMOps, model evaluation, or AI governance in production. • Experience with Java, Node.js, or .NET technology stacks and modern web application architectures. • Experience operating in regulated, data-sensitive, or sovereign environments. • Relevant cloud or architecture certifications where they reflect current, practical capability. Inception42 is building AI systems designed for practical deployment at scale — across industries, infrastructure, and national-level initiatives. If you want to work close to both advanced AI capability and real-world execution, we’d like to hear from you. Show more Show less

Define the architectural vision and target state for enterprise AI, data, and digital solutions. Translate business, product, and operational requirements into end-to-end solution designs, reference architectures, and implementation guidance. Design secure, resilient, observable, and scalable systems across public cloud, private cloud, hybrid, and sovereign environments. Evaluate build-versus-buy decisions and technology options using clear criteria for value, risk, operability, portability, and long-term ownership. Shape architectures for AI and generative AI workloads, including model services, retrieval-augmented generation, data pipelines, evaluation, monitoring, and lifecycle controls. Define integration patterns for APIs, microservices, event-driven systems, data-intensive applications, identity, and enterprise platforms. Identify opportunities to reuse platform capabilities, architecture patterns, and shared services across products and deployments. Drive architecture reviews and technical decisions, documenting trade-offs and resolving dependencies across teams and systems. Partner with security and governance teams to embed privacy, data sovereignty, access control, web security, auditability, and responsible AI requirements by design. Support discovery, solution shaping, technical proposals, proof-of-value work, estimation, and delivery planning with credible architecture and risk assumptions. Stay close to implementation, validating that delivered systems remain aligned with the intended architecture and production requirements. Raise the technical bar through mentoring, practical design feedback, and reusable standards for engineers and solution teams.