Senior LLM & ML Infrastructure Engineer (Remote)

artificial intelligence and robotics laboratory (itu air lab); Arlington (VA); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Air seeks an experienced Senior Machine Learning Engineer to shape the infrastructure powering its language models and AI systems. You will own LLMOps, fine-tuning platforms, dataset pipelines, experiment management, and production observability, ensuring reproducibility and reliability across the full model lifecycle.

You’ll build platforms enabling rapid experimentation by AI engineers and researchers, while coordinating across training, evaluation, deployment, and monitoring to support

Qualifications

• 5+ years building production ML systems and ML infrastructure.
• Experience with LLM fine-tuning and post-training workflows.
• Experience with AWS/GCP/Azure and GPU infrastructure.
• Ability to work across ML experimentation and infrastructure engineering.

Responsibilities

• Design and build LLMOps infrastructure for production models.
• Build scalable training and fine-tuning infra for language models.
• Develop pipelines for supervised fine-tuning and other post-training techniques.
• Create data pipelines for training, evaluation, and synthetic data generation.
• Develop experiment management infrastructure and model versioning.
• Build model-serving and observability for training and inference.
• Optimize workloads for GPU utilization and cost.

Skills

Production ML infra

LLM fine-tuning

Python

Kubernetes

Cloud platforms

Observability

Distributed systems

Model serving

Experimentation pipelines

Tools

Docker

Terraform

Job description

Air seeks an experienced Senior Machine Learning Engineer to shape the infrastructure powering its language models and AI systems. You will own LLMOps, fine-tuning platforms, dataset pipelines, experiment management, and production observability, ensuring reproducibility and reliability across the full model lifecycle.

You’ll build platforms enabling rapid experimentation by AI engineers and researchers, while coordinating across training, evaluation, deployment, and monitoring to support