AI Infrastructure Engineer
Posted 27 Jul 2026
Advertised as “AI Infrastructure Engineer III”
BranchFactor Summary
The original posting with the fluff stripped out
- Level
- III
- Expected experience
- 4–6 yrs
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Full-time
No salary listed for this role.
About Mozn
MOZN is a leading Enterprise AI company enabling organizations to make informed decisions in two critical domains: Financial Crime Prevention and Enterprise Knowledge Intelligence.
We’re a diverse, collaborative team of innovators united by a shared purpose: to build AI that delivers tangible business value, builds trust, and empowers people and organizations with augmented intelligence. Our culture is built on the relentless pursuit of excellence and meaningful impact.
If you’re passionate about working alongside exceptional talent on world-class AI, and you want the autonomy and runway to do the best work of your career, join us in shaping the future of intelligent enterprises.
About the role
We are looking for a highly motivated AI Infrastructure Engineer III to join our Cloud Engineering team. The ideal candidate is passionate about building scalable AI infrastructure that enables machine learning engineers and data scientists to efficiently develop, train, deploy, and operate AI models.
This role focuses on designing, operating, automating, and continuously improving AI platform capabilities including Kubeflow, MLflow, GPU infrastructure, distributed training platforms, model serving infrastructure, and MLOps tooling across cloud-native and hybrid environments.
The ideal candidate possesses deep expertise in AI infrastructure and GPU platforms. Kubernetes and cloud platform experience are essential enablers, but the primary focus is building highly optimized infrastructure for AI and machine learning workloads.
What you'll do
AI Platform Engineering
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Design, deploy, and operate enterprise AI/ML platforms.
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Build self-service platforms for Data Scientists and ML Engineers.
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Deploy and operate Kubeflow, MLflow, KServe, Ray, or similar AI platforms.
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Design infrastructure supporting model training, experimentation, feature engineering, and inference.
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Build highly available and scalable model serving infrastructure.
GPU Infrastructure
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Design and operate GPU clusters for large-scale AI workloads.
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Optimize GPU scheduling, utilization, sharing, autoscaling, and resource allocation.
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Deploy and manage NVIDIA GPU Operator and GPU-enabled Kubernetes environments.