Job Title
LLM Infrastructure Engineer
Location
Remote - US Time Zones (EST- PST)
Job Type
Full-time
Role Overview
We are seeking an LLM Infrastructure Engineer with hands-on experience building the systems that power large language models. This is a full-time, hands-on engineering role focused on designing, deploying, and scaling the infrastructure behind model training, fine-tuning, and inference. You will work closely with machine learning and engineering teams to make sure models run reliably, efficiently, and cost-effectively in production. The idóneo candidate understands both the demands of LLM workloads and the cloud systems needed to support them at scale.
Key Responsibilities
- Design, build, and maintain infrastructure for training, fine-tuning, and serving large language models
- Deploy and scale LLM inference services with a focus on latency, throughput, and reliability
- Manage GPU compute resources and optimize workload scheduling and utilization
- Build and maintain data pipelines that support 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
- Proactively identify bottlenecks, investigate root causes, and resolve infrastructure issues

Required Qualifications
- 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 containerization and orchestration tools such as Docker and Kubernetes
- Solid understanding of distributed systems, GPU computing, and performance optimization
- Strong written and spoken English

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