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Job Title: LLM Infrastructure EngineerLocation | RemoteTime 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 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 with a clear, professional communication style
Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK‐based companies.
Applications without this experience will not be considered.
Preferred Qualifications
Strong AWS experience, including services such as EC2, EKS, S3, and SageMaker
Prior experience working at a large company or within enterprise‐scale engineering environments
Familiarity with LLM serving frameworks such as vLLM, TensorRT-LLM, or Triton Inference Server
Experience with infrastructure‐as‐code tools such as Terraform or CloudFormation
Experience with distributed training frameworks such as PyTorch Distributed, DeepSpeed, or Ray
Tools & Technology
AWS
Python
Docker and Kubernetes
Terraform or CloudFormation
PyTorch and LLM serving frameworks
CI/CD and monitoring tools
Slack, Zoom, and other remote collaboration tools
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📌 Llm Infrastructure Engineer (Buenos Aires)
🏢 Hire Hangar
📍 Buenos Aires