Hire Hangar
Skills: Python, AWS, Docker, CI/CD
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Job Title: LLM Infrastructure Engineer
Location | Remote
Time Zone | US Time Zones (EST–PST)
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 ideal 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 Kuberne…
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