Senior Applied AI Solutions Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Applied AI Solutions Engineer based in United Kingdom. This role sits at the intersection of advanced ML engineering, customer success, and product development within a rapidly evolving AI infrastructure environment. You will help enterprise customers move complex machine learning workloads from proof of concept into production. Around half of your time will be spent working directly with customers, solving technical challenges and accelerating their adoption of the platform. The remainder will focus on building prototypes, exploring emerging AI techniques, and demonstrating new possibilities across the product portfolio. Your hands-on field experience will directly influence product priorities, helping transform recurring customer challenges into concrete improvements. You will work across areas including inference, databases, MLOps, serverless AI, agentic architectures, and specialized AI applications. This is a highly autonomous role for an engineer who wants technical depth, customer impact, and a direct voice in the evolution of AI products. Accountabilities - Build polished prototypes and technical demonstrations across serverless inference, databases, MLflow, MLOps, and applied AI use cases, including Physical AI and healthcare and life sciences. - Support enterprise customers hands-on through proof-of-concept design, technical onboarding, validation, and the transition from experimentation to production. - Act as a technical bridge between customer ML teams and the platform, helping diagnose challenges and accelerate time-to-value during the first months of adoption. - Research emerging applied AI techniques, including new training approaches, inference optimizations, agentic architectures, and frameworks, and turn relevant discoveries into working prototypes.

- Translate research and customer experience into technical write-ups, recommendations, and actionable product feedback. - Identify recurring issues across customer deployments and provide specific, evidence-based input that can shape the product roadmap. - Develop reusable technical assets such as notebooks, reference architectures, benchmarks, and implementation guides to reduce onboarding friction. - Collaborate with sales, product, engineering, and customer teams to ensure technical insights are converted into practical solutions and product improvements. - Track developments across the AI ecosystem and help the wider team anticipate important applied AI trends over the next 6–12 months. Requirements - Strong hands-on experience with modern machine learning systems, including fine-tuning large models, debugging distributed training workloads, building production RAG or agentic pipelines, and optimizing GPU-based inference. - Fluency across the modern ML technology stack, including PyTorch, Hugging Face, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent platforms, and vector databases. - Experience working directly with enterprise ML teams, whether in a solutions engineering, customer engineering, ML engineering, or closely related capacity. - Demonstrated ability to research emerging techniques, read technical and academic papers, and translate relevant findings into working implementations. - Strong understanding of ML infrastructure and the ability to troubleshoot complex workloads across the stack. - Excellent communication skills, with the ability to adapt technical explanations for audiences ranging from ML engineers to senior technology and business leaders. - Strong customer orientation combined with the ability to maintain technical depth and engineering rigor. - Ability to work effectively in a highly varied environment, moving between customer problems, technical research, rapid prototyping, and product feedback.