We are hiring an AI Infrastructure / ML Engineer to help optimize CapaCloud for AI workloads, model deployment, training, inference, and scalable GPU utilization.You will work closely with infrastructure engineers to ensure the platform supports modern AI workflows for startups, researchers, and enterprise users.This role is ideal for someone passionate about AI systems, MLOps, and large-scale GPU computing. Key ResponsibilitiesBuild and optimize AI deployment pipelinesImprove GPU workload efficiency for AI applicationsSupport AI training and inference infrastructureOptimize performance for PyTorch, TensorFlow, and LLM workloadsBuild scalable APIs and inference systemsDevelop benchmarking and performance testing toolsCollaborate with infrastructure teams on orchestration systemsSupport model deployment and containerized AI workloadsImprove developer experience for AI usersMonitor and optimize AI compute performance Required Skills & ExperienceExperience with AI/ML infrastructure and MLOpsStrong Python programming skillsExperience with PyTorch, TensorFlow, or JAXExperience with GPU computing and CUDA environmentsFamiliarity with containerized deployment systemsExperience deploying AI models in productionUnderstanding of inference optimization techniquesExperience with APIs and backend systemsStrong debugging and analytical skills Nice To HaveExperience with LLM infrastructureFamiliarity with Hugging Face ecosystemExperience with distributed training systemsKnowledge of Kubernetes and orchestration systemsExperience with AI inference optimization toolsOpen-source AI contributions What Success Looks LikeHigh-performance AI deployment infrastructureOptimized GPU utilization for AI workloadsSmooth onboarding for AI developersReliable inference and training systemsStrong benchmark performance across workloads Employment TypeFull-timeRemote