Production Engineer - Applied Machine Learning

What you'll do

• Manage production stability across scheduling, orchestration, Kubernetes/GPU clusters, distributed training, inference serving, and storage systems.
• Build reliability mechanisms including SLO/SLA, observability, alerting, on-call processes, incident response, and disaster recovery.
• Improve engineering operations and resource efficiency through automation, release controls, capacity forecasting, scaling, and cost governance.

What they ask for

• Bachelor’s degree or above in Computer Science, Software Engineering, Artificial Intelligence, or a related field.
• Familiarity with Linux and proficiency in at least one of Shell, Python, Go, or C++.
• Understanding of machine learning training/inference architectures, Kubernetes, GPU clusters, or distributed storage systems.
• Proven experience in online troubleshooting, performance analysis, and building automation platforms.

Nice to have

• Experience with large-scale training/inference/storage platforms, SLO governance, FinOps, NoSQL, or open-source infrastructure.

Tools

Linux, Shell, Python, Go, C++, Kubernetes, GPU clusters, NoSQL

AI focus

Supports Applied Machine Learning training and inference systems and recommendation-based algorithms and platforms.