Senior Solution Architect - Germany

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. It is 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 in our offices in Munich, Germany . 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: Fluent in French