LLM Infrastructure Engineer

📋 Job Overview

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

🏢 About Hire Hangar

Hire Hangar connects top talent with vetted employers, competitive pay, and real growth opportunities.

🎯 The Role

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.

⭐ Desirable Experience

• 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

🎁 Benefits

Compensation: Tier 1 $2.5K – $3K per month or Tier 2 $3K – $4K per month. We 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.

🛂 Visa & Eligibility

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.