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.