Hire Hangar - LLM Infrastructure Engineer
Requirements
• 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 • Tools & Technology: • Docker and Kubernetes • Terraform or CloudFormation • PyTorch and LLM serving frameworks • CI/CD and monitoring tools • Slack, Zoom, and other remote collaboration tools • 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
• 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.
Benefits
• Tier 1$2.5K – $3K per month • Tier 2$3K – $4K per month • Compensation PhilosophyWe believe in attracting and retaining the very best talent by offering highly competitive, transparent compensation. The salary range we post is the real deal—we don’t bury hidden figures or negotiate down once you’re in the door. We’re committed to rewarding performance and results, ensuring your pay reflects the value you bring. Our goal is simple: offer competitive, market-leading compensation to drive a culture of excellence where top performers thrive. If you see a salary range with us, you can trust that’s exactly what we’ll provide to qualified candidates—no surprises, no gimmicks. • highly competitive, transparent compensation • offer competitive, market-leading compensation • 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.
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