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source · wttj·req · jb_49bf1edb80·listed 6h ago
Mid/Senior Solution Architect
Multiverse Computing·London, United Kingdom·Hybrid·Full-time
Sourced listing · wttjNo salary disclosed
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Summary
the pitchJoin our pre-sales team as a Mid/Senior Solution Architect in London, UK. You will bridge the gap between our technology and our customers' needs, crafting innovative AI-powered solutions. The ideal candidate will have experience in a technical partner pre-sales or consulting role, strong knowledge of cloud platforms and AI/ML services, hands-on coding skills in Python and SQL, and excellent communication and presentation skills.
Role
posted by company- 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)
- Experience sizing GPU infrastructure for LLM inference or training workloads (memory, throughput, hardware tiers from A10 to H200)
- 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
- Hands-on coding skills in Python, SQL, and familiarity with ML libraries and frameworks (PyTorch, TensorFlow, Hugging Face)
- Understanding of LLM inference stacks (vLLM, llama.cpp, OpenVINO) and model delivery formats (ONNX, .safetensors, HuggingFace model hub)
- 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
- 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
- Must be available to travel as needed for meetings, conferences, and project requirements
- Bachelor's or master's degree in Computer Science, Data Science, Engineering, or related field
- Experience benchmarking and evaluating LLM performance (accuracy, latency, throughput)
- Languages: English
- 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 EU AI Act