Powering the systems behind AI-driven products
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 Responsibilities
Build and optimize AI deployment pipelines
Improve GPU workload efficiency for AI applications
Support AI training and inference infrastructure
Optimize performance for PyTorch, TensorFlow, and LLM workloads
Build scalable APIs and inference systems
Develop benchmarking and performance testing tools
Collaborate with infrastructure teams on orchestration systems
Support model deployment and containerized AI workloads
Improve developer experience for AI users
Monitor and optimize AI compute performance
Required Skills & Experience
Experience with AI/ML infrastructure and MLOps
Strong Python programming skills
Experience with PyTorch, TensorFlow, or JAX
Experience with GPU computing and CUDA environments
Familiarity with containerized deployment systems
Experience deploying AI models in production
Understanding of inference optimization techniques
Experience with APIs and backend systems
Strong debugging and analytical skills
Nice To Have
Experience with LLM infrastructure
Familiarity with Hugging Face ecosystem
Experience with distributed training systems
Knowledge of Kubernetes and orchestration systems
Experience with AI inference optimization tools
Open-source AI contributions
What Success Looks Like
High-performance AI deployment infrastructure
Optimized GPU utilization for AI workloads
Smooth onboarding for AI developers
Reliable inference and training systems
Strong benchmark performance across workloads
Employment Type
Full-time
Remote
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