Senior Applied AI Solutions Engineer

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. Experience in Physical AI, robotics, simulation, healthcare and life sciences, drug discovery, medical imaging, clinical NLP, or enterprise AI application development is an advantage. Familiarity with large-scale MLOps technologies such as Kubeflow, Metaflow, Argo, or Ray is a plus. Previous experience at a cloud provider or AI infrastructure company is beneficial. Publicly shared technical work, such as useful notebooks, talks, technical articles, or open-source contributions, is a strong plus. Benefits Competitive compensation, with the advertised base compensation range of $200,000–$350,000 USD , depending on experience, skills, qualifications, level, and location. Comprehensive benefits package. Flexible working arrangements with significant ownership and autonomy. Professional growth, continuous learning, and career development opportunities. Opportunity to work on impactful AI and ML infrastructure projects. Collaborative, innovative, and fast-moving working environment. International environment with talented teams distributed across multiple locations. Opportunity to work directly with enterprise customers and influence product direction. Exposure to emerging areas of applied AI, including generative AI, agentic systems, MLOps, inference optimization, Physical AI, and healthcare applications. An environment that values initiative, technical excellence, trust, and meaningful ownership. Inclusive workplace committed to equal employment opportunities and a diverse, supportive culture.