Solutions Architect (AI/LLM/GPU)

A fast-growing European deep-tech company is seeking a Solution Architect to join its pre-sales team in Munich.

You will connect advanced AI technology with real customer needs, shaping scalable solutions for organisations operating across sectors such as finance, energy, manufacturing, telecommunications and industry.

This is a customer-facing position for someone who combines strong AI and cloud knowledge with the ability to explain complex concepts, deliver compelling demonstrations and design practical deployment architectures.

What you'll do

• Translate customer and partner requirements into robust AI solution architectures.
• Lead technical discussions with both engineering teams and business stakeholders.
• Deliver hands-on product demonstrations independently.
• Prepare technical proposals and contribute to RFP and tender responses.
• Design solutions spanning public cloud, sovereign cloud, on-premise and hybrid environments.
• Advise on infrastructure sizing for LLM inference and training workloads.
• Benchmark AI models across accuracy, latency and throughput.
• Support the deployment and operationalisation of AI workloads using modern MLOps practices.
• Travel when required for customer meetings, conferences and project activities.
• What you'll bring
• Experience in a customer- or partner-facing technical pre-sales or consulting role, such as Solution Architect, Sales Engineer or Implementation Consultant.
• Excellent presentation and communication skills across technical and non-technical audiences.
• Experience producing technical proposals or tender responses and independently running product demonstrations.
• Strong knowledge of AWS, Azure or Google Cloud and their AI/ML services.
• Understanding of sovereign, on-premise and hybrid deployment models.
• Familiarity with MLOps practices, including CI/CD, monitoring and orchestration.
• Practical knowledge of Docker and Kubernetes for AI workloads.
• Understanding of LLM inference technologies and model-delivery formats such as ONNX and safetensors.
• Experience sizing GPU infrastructure for LLM inference or training.
• Experience evaluating model accuracy, latency and throughput.
• Hands-on Python and SQL skills, together with familiarity with frameworks such as Py Torch, Tensor Flow and Hugging Face.
• A bachelor's or master's degree in computer science, data science, engineering or a related discipline.
• Fluent German and English.
• Availability to travel when needed.
• Computer vision, speech, vision-language or other multimodal models.
• RAG pipelines or multi-agent systems.
• Batch and streaming data architectures and big-data technologies.
• AI privacy, ethics and regulation, including GDPR and the EU AI Act.
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