Senior ML Engineer — LLMOps & Production AI (Remote)

Air; Oregon; Hybrid; USD 170,000 - 240,000; Full time

Job summary

Air is seeking a Senior Machine Learning Engineer to build the infrastructure for language models and AI systems, enabling rapid experimentation from training to deployment and observability. You will own end-to-end ML lifecycle, including datasets, evaluation, model serving, and production reliability.

This engineering-heavy role blends ML research with infrastructure, supporting teams to move models from experimentation to production at scale.

Qualifications

• 5+ years building production ML systems and infrastructure.
• Experience with LLM fine-tuning and post-training workflows.
• Strong Python programming for production-grade software.
• Experience with AWS, GCP, Azure and GPU-based workloads.

Responsibilities

• Design and build LLMOps infrastructure for production models.
• Build scalable training and fine-tuning infra for LLMs.
• Develop pipelines for supervised fine-tuning and parameter-efficient methods.
• Create data pipelines for training, evaluation, and synthetic data generation.
• Implement model registries, artifact management, and versioning.
• Develop experiment management infrastructure for model comparisons.
• Build automated evaluation pipelines for readiness for production.
• Design observability for training and inference, including metrics and logs.
• Optimize training/inference for GPU utilization and cost.
• Support product/AI engineering integration with multiple models/providers.

Skills

Production ML systems

Python programming

Kubernetes & containers

Cloud platforms

ML pipeline tooling

Model versioning & experiments

LLM fine-tuning workflows

Distributed systems

Observability

Tools

Docker

MLflow

Kubernetes

Terraform

Git

Job description

Air is seeking a Senior Machine Learning Engineer to build the infrastructure for language models and AI systems, enabling rapid experimentation from training to deployment and observability. You will own end-to-end ML lifecycle, including datasets, evaluation, model serving, and production reliability.

This engineering-heavy role blends ML research with infrastructure, supporting teams to move models from experimentation to production at scale.