Multiverse Computing
Multiverse Computing is a fast-growing deep-tech company founded in 2019 and recognized by CB Insights as one of the 100 most promising AI companies globally. We are the largest quantum software company in the EU, with 250+ employees worldwide building advanced AI and quantum solutions that help enterprises tackle complex, high-impact challenges across industries such as finance, energy, manufacturing, telecom, and industrials.
Our mission is to enable organizations to gain a meaningful competitive edge through cutting-edge AI and quantum technologies.
Why join us?
We are a European deep-tech leader in quantum and AI, backed by major global strategic investors and strong EU support. Our groundbreaking technology is already transforming how AI is deployed worldwide — compressing large language models by up to 95% without losing accuracy and cutting inference costs by 50–80%. Joining us means working on cutting-edge solutions that make AI faster, greener, and more accessible — and being part of a company often described as a “quantum-AI unicorn in the making.”
This opportunity is to work from Qatar. We are looking for a talented Solutions Architect to join our pre-sales team and bridge the gap between our technology and our customers’ needs, crafting innovative, scalable, and robust AI- powered solutions.
Key requirements:
- Previous experience in a technical partner pre-sales or consulting role with a heavy emphasis on partner and customer-facing interactions (i.e. Solutions Architect, Sales Engineer, Implementation Consultant)
- Excellent communication and presentation skills, able to interface effectively with technical and non-technical stakeholders; experience writing technical proposals or responding to RFPs/tenders; experience running hands-on product demos independently.
- Strong knowledge of cloud platforms (AWS, Azure, GCP) and AI/ML services (e.g., SageMaker, Vertex AI, AzureML), including sovereign, on-premise, and hybrid deployment models.
- Familiarity with MLOps tools and practices: CI/CD, monitoring, and orchestration frameworks (e.g., Kubeflow, Flyte, MLflow); proficiency with Docker and Kubernetes for AI workload containerization.
- Understanding of LLM inference stacks (vLLM, llama.cpp, OpenVINO) and model delivery formats (ONNX, .safetensors, HuggingFace model hub).
- Experience sizing GPU infrastructure for LLM inference or training workloads (memory, throughput, hardware tiers from A10 to H200).
- Experience benchmarking and evaluating LLM performance (accuracy, latency, throughput).
- Hands-on coding skills in Python, SQL, and familiarity with ML libraries and frameworks (PyTorch, TensorFlow, Hugging Face).
- Bachelor's or master's degree in Computer Science, Data Science, Engineering, or related field.
- Must be available to travel as needed for meetings, conferences, and project requirements.
- Languages: Arabic and English
Preferred Qualifications:
- Experience with Computer Vision models, Speech models, Vision-Language models, and other modalities.
- Experience with AI model optimization, quantization, or deployment to edge devices.
- Hands-on experience designing RAG pipelines and/or multi-agent systems..
- Experience designing data architectures (batch & streaming) and working with big data technologies.
- Knowledge of data privacy and ethical considerations in AI, including GDPR compliance and familiarity with the respective country AI Act.
Location: Applicants must have legal authorization to work in the country where the position is based
Perks & Benefits:
- Equal pay guaranteed.
- Signing bonus.
- Relocation package (if applicable).
- Eligibility for educational budget according to internal policy.
- Hybrid opportunity.
- Flexible working hours.
- Language classes and discounted lunch options
- Working in a high paced environment, working on cutting edge technologies.
- Career plan. Opportunity to learn and teach.
As an equal opportunity employer, Multiverse Computing is committed to building an inclusive workplace. The company welcomes people from all different backgrounds, including age, citizenship, ethnic and racial origins, gender identities, individuals with disabilities, marital status, religions and ideologies, and sexual orientations to apply.
TECHNICAL & MARKET ANALYSIS | Appended by Quantum.Jobs
The role of a Solution Architect within the deep-tech ecosystem serves as the primary bridge between highly specialized algorithmic innovation and the operational realities of global enterprise infrastructure. This function is structurally necessary to resolve the integration complexity inherent in deploying advanced AI and quantum-ready technologies across diverse sovereign and hybrid cloud environments. By translating complex scientific breakthroughs into scalable, industrial-grade architectures, this role facilitates the practical adoption of frontier technologies within high-compute sectors. Market signals indicate that as deep-tech matures, the demand for architects who can navigate the interoperability between classical high-performance computing and emerging quantum-classical hybrid systems is a critical determinant of commercial utility. This position ensures that emerging technologies are not merely theoretical advantages but are architecturally compatible with the rigorous demands of enterprise-scale production.
The deep-tech value chain is currently navigating a decisive shift from laboratory-scale proof-of-concepts to the integration of high-fidelity computational kernels within global industry ecosystems. Within this landscape, the Solution Architect functions as a high-leverage stabilization point, managing the interface between cutting-edge AI software and the complex hardware infrastructures required for deployment. While the sector continues to advance in model efficiency and hardware modalities, the primary bottleneck for wide-scale industrial adoption has shifted to the architectural layer, specifically regarding the reproducibility, scalability, and security of hybrid workflows.
Sector-wide efforts continue to address talent and integration challenges as organizations move toward sovereign and on-premise deployment models to satisfy data sovereignty mandates. This is particularly evident in regions like Qatar, where national technology strategies emphasize the development of local deep-tech capabilities and secure digital infrastructures. The ecosystem requires specialized architects who can navigate the fragmentation of the AI software stack and the lack of standardized benchmarking protocols across disparate cloud platforms.
Current industry dynamics, influenced by public-private funding cycles and national security interests, place a premium on roles that can drive interoperability between specialized deep-tech solutions and legacy enterprise resource planning systems. As the value chain evolves, the availability of architects capable of orchestrating these complex cross-functional dependencies—ranging from GPU infrastructure sizing to MLOps lifecycle management—is a primary factor in whether a commercial organization can successfully transition from exploration to deployment at scale. Multiverse Computing represents a key entity in this transition, emphasizing the necessity of bridging these technical gaps to maintain momentum in the global technology race.
The capability architecture for this role centers on the synchronization of advanced model optimization techniques with the protocols of enterprise-grade systems engineering. Mastery of the hardware-agnostic software layer is essential for ensuring that deep-tech applications are optimized for the specific constraints of diverse inference stacks, including vLLM and OpenVINO. This requires a deep understanding of the integration points between high-level application interfaces and the underlying orchestration frameworks that manage containerized workloads in hybrid environments.
These capabilities are fundamental to the throughput of technology organizations, as they enable the parallelization of pre-sales research with the development of scalable cloud architectures. By establishing rigorous verification and validation frameworks, this function provides the leverage needed to assess the true business value of AI and quantum-enhanced solutions before full-scale capital allocation. Furthermore, the ability to manage complex stakeholder landscapes ensures that technical outputs are reconciled with the practical constraints of regulatory compliance and data sovereignty. Such expertise reduces the iteration friction between abstract innovation and product delivery, which is critical for long-term interoperability within the emerging global market. - Accelerates the deterministic transition from frontier deep-tech research to industrial-grade enterprise applications
- Mitigates systemic execution risks by synchronizing long-term innovation cycles with near-term technology roadmaps
- Facilitates the integration of advanced computational kernels into standardized cloud and high-performance computing infrastructures
- Strengthens the reliability of organizational technology strategies through the implementation of rigorous performance benchmarking
- Reduces iteration friction between fundamental algorithm breakthroughs and the deployment of scalable software architectures
- Optimizes the allocation of specialized technical talent across pre-sales, development, and strategic liaison portfolios
- Enhances the stability of the deep-tech value chain by providing predictable requirement frameworks for external partners
- Supports the scaling of AI capabilities by managing the complex dependencies of hybrid classical-quantum workflows
- Improves the transparency of technology readiness level progression for stakeholders in the investment and policy sectors
- Enables the structural reproducibility of technical implementations through the standardization of architectural protocols
- Protects high-capital research and development investments by ensuring alignment between scientific discovery and commercial scalability
- Orchestrates the convergence of specialized innovation pathways with the practical demands of global enterprise-ready servicesIndustry Tags: Deep Tech, Solution Architecture, Hybrid Cloud, AI Infrastructure, MLOps, Quantum-AI Integration, Sovereign Cloud, Enterprise Software, LLM Inference, Digital Transformation
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