AI Infrastructure

Job Title: AI Infrastructure / ML Engineer

Location: Wyoming Lagos,
Employment Type: Full-time

Description

• This role is ideal for someone passionate about AI systems, MLOps, and large-scale GPU computing.
• You will work closely with infrastructure engineers to ensure the platform supports modern AI workflows for startups, researchers, and enterprise users.
• We are hiring an AI Infrastructure / ML Engineer to help optimize CapaCloud for AI workloads, model deployment, training, inference, and scalable GPU utilization.

Key Responsibilities

• Improve developer experience for AI users
• Support AI training and inference infrastructure
• Monitor and optimize AI compute performance.
• Build scalable APIs and inference systems
• Build and optimize AI deployment pipelines
• Support model deployment and containerized AI workloads
• Improve GPU workload efficiency for AI applications
• Collaborate with infrastructure teams on orchestration systems
• Develop benchmarking and performance testing tools
• Optimize performance for PyTorch, TensorFlow, and LLM workloads

What Success Looks Like

• Optimized GPU utilization for AI workloads
• Smooth onboarding for AI developers
• Strong benchmark performance across workloads.
• Reliable inference and training systems
• High-performance AI deployment infrastructure

Required Skills & Experience

• Strong debugging and analytical skills
• Understanding of inference optimization techniques
• Experience with APIs and backend systems
• Interested candidates should possess a Master`s Degree
• Strong understanding.
• Strong Python programming skills
• Experience in startup or B2B SaaS marketing
• Experience with PyTorch, TensorFlow, or JAX
• Experience with AI/ML infrastructure and MLOps
• Experience deploying AI models in production
• Experience with GPU computing and CUDA environments
• Familiarity with containerized deployment systems

Nice To Have:

• Experience with distributed training systems
• Familiarity with Hugging Face ecosystem
• Open-source AI contributions.
• Knowledge of Kubernetes and orchestration systems
• Experience with AI inference optimization tools
• Experience with LLM infrastructure