LLM Infrastructure Engineer

Short Summary

LLM Infrastructure Engineer at hirehangar. Join Hire Hangar and work with fast-growing global companies while building a long-term career. 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…

Key Details

• Position / opportunity: LLM Infrastructure Engineer
• Organization: hirehangar
• Country / coverage: Argentina
• Location: Argentina, Argentina
• Work arrangement: Remote
• Opportunity type: Remote work opportunities
• Sector: Information Technology
• Compensation: Not specified
• Application deadline: Not specified by source

Description

Join Hire Hangar and work with fast-growing global companies while building a long-term career.

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 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

Please NOTE:

It is crucial that you complete the application form in full. As part of the application process, you will be required to record a video. If your application is successful, you will receive an email confirming next steps—the video is the first step of the interview process. If you do not record a video, we will not be able to consider you for ANY open roles.

We connect top talent with vetted employers, competitive pay, and real growth opportunities.

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

Requirements / Eligibility

• – 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.
• – 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

How To Apply

Select Apply now to complete the application for this position. Submit the documents requested in the application form.