LLM Infrastructure Engineer

• Design, build, and maintain infrastructure for training, fine-tuning, and serving large language models
• Deploy and scale LLM inference services for latency, throughput, and reliability
• Manage GPU compute resources and optimize workload scheduling and utilization
• Build and maintain data pipelines for model training and evaluation
• Implement monitoring, logging, and alerting for model performance and system health
• Optimize infrastructure costs while maintaining performance and availability
• Automate deployment workflows using infrastructure-as-code and CI/CD practices
• Collaborate with ML engineers and researchers to move models from experimentation into production
• Identify bottlenecks, investigate root causes, and resolve infrastructure issues

• Proven experience building large language model infrastructure (non-negotiable)
• Hands-on experience deploying and serving machine learning models in production environments
• Strong programming skills in Python, with experience in systems or backend development
• Experience with Docker and Kubernetes
• Solid understanding of distributed systems, GPU computing, and performance optimization
• Strong written and spoken English with a clear, professional communication style
• Prior remote work experience
• Fluency with remote collaboration tools and platforms such as Slack, Zoom, Google Workspace, Asana, or similar
• Ideally worked with US or UK-based companies
• Strong AWS experience, including EC2, EKS, S3, and SageMaker
• Prior experience at a large company or in enterprise-scale engineering environments
• Familiarity with vLLM, TensorRT-LLM, or Triton Inference Server
• Experience with Terraform or CloudFormation
• Experience with PyTorch Distributed, DeepSpeed, or Ray

• Highly competitive, transparent compensation
• Competitive, market-leading compensation
• Long-term career opportunity
• Growth opportunities