AI Infrastructure / ML Engineer

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