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LLM Infrastructure Engineer
Hire Hangar Global. Design, build, and maintain infrastructure for training, fine-tuning, and serving large language models .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and maintaining infrastructure for large language models, with strong capabilities in deploying machine learning models in production environments. Proficient in optimizing GPU resources and automating workflows using infrastructure-as-code practices.
Highest-signal resume keywords
Large Language Model InfrastructurePython ProgrammingAWS ExperienceDocker and KubernetesPerformance Optimization
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Large Language Model InfrastructurePython ProgrammingPerformance OptimizationDistributed SystemsGPU ComputingMachine Learning Model DeploymentData Pipeline DevelopmentMonitoring and LoggingInfrastructure AutomationModel Evaluation
Soft Skills
Professional CommunicationRemote Collaboration
Tools & Technologies
DockerKubernetesAWS EC2AWS EKSAWS S3AWS SageMakerTerraformCloudFormationSlackZoom
Industry Keywords
Enterprise-Scale EngineeringRemote Work ExperienceML Engineers CollaborationBottleneck IdentificationRoot Cause Investigation
Tech Stack
Tools & technologiesAWSDistributed SystemsDockerEC2KubernetesPythonPyTorchRayTerraform
About the role
Key responsibilities & impact- Design, build, and maintain infrastructure for training, fine-tuning, and serving large language models
- Deploy and scale LLM inference services for latency, throughput, and reliability
- Manage GPU compute resources and optimize workload scheduling and utilization
- Build and maintain data pipelines for 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
- Identify bottlenecks, investigate root causes, and resolve infrastructure issues
Requirements
What you’ll need- 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 Docker and Kubernetes
- Solid understanding of distributed systems, GPU computing, and performance optimization
- Strong written and spoken English with a clear, professional communication style
- Prior remote work experience
- Fluency with remote collaboration tools and platforms such as Slack, Zoom, Google Workspace, Asana, or similar
- Ideally worked with US or UK-based companies
- Strong AWS experience, including EC2, EKS, S3, and SageMaker
- Prior experience at a large company or in enterprise-scale engineering environments
- Familiarity with vLLM, TensorRT-LLM, or Triton Inference Server
- Experience with Terraform or CloudFormation
- Experience with PyTorch Distributed, DeepSpeed, or Ray
Benefits
Comp & perks- Highly competitive, transparent compensation
- Competitive, market-leading compensation
- Long-term career opportunity
- Growth opportunities