LLM Infrastructure Engineer
At a glance
Mid-level AI Engineering role at Hire Hangar. Remote, Argentina · contract.
Pay not stated Infrastructure Engineer salary
remoteroles summary, based on the employer's posting.
What you'll do
- Build and operate the infrastructure used to train, fine-tune and serve large language models
- Scale inference services while balancing response speed, throughput and dependable operation
- Manage GPU capacity and scheduling, and improve how workloads use available compute
- Create training and evaluation data pipelines, plus monitoring and alerts for models and systems
- Automate deployments, control infrastructure costs and troubleshoot bottlenecks with ML and research teams
What you bring
- You have hands-on experience building infrastructure for large language models, a stated non-negotiable
- You have deployed machine learning models into production and can build with Python in systems or backend work
- You bring experience with containers and orchestration, such as Docker and Kubernetes, plus distributed systems and GPU performance
- You communicate professionally in spoken and written English and have worked remotely using collaboration platforms
Who this fits
This is a hands-on engineering role focused on taking model work into production, rather than a people-management position. It is remote and requires availability across US time zones, from EST to PST. Prior remote work is required, and experience with US- or UK-based companies is preferred. Applicants must complete the form and record a video as the first interview step.
From the employer
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.
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