AI Infrastructure Engineer

AI Infrastructure Engineer Position Overview

We are seeking an
AI Infrastructure Engineer to design, build, and scale the infrastructure that powers our artificial intelligence and machine learning workloads. This role sits at the intersection of
AI/ML, cloud infrastructure, distributed systems, and Dev Ops/MLOps
.

The ideal candidate has experience building highly available, scalable infrastructure for training, deploying, and operating machine learning and generative AI applications. You will partner closely with Machine Learning Engineers, Data Scientists, Software Engineers, and Platform Engineering teams to ensure AI workloads can run reliably, securely, and efficiently at scale.

• Design, build, and maintain scalable infrastructure for
  AI, machine learning, and Generative AI workloads
• Build and manage cloud infrastructure across
  AWS, Azure, and/or Google Cloud Platform
• Deploy and operate GPU-based compute environments for model training and inference
• Design infrastructure supporting
  LLMs, model training, fine-tuning, inference, and AI applications
• Build and manage containerized workloads using
  Docker and Kubernetes
• Develop infrastructure-as-code using tools such as
  Terraform, Cloud Formation, or Pulumi
• Build CI/CD and MLOps pipelines supporting model development and deployment
• Optimize GPU/CPU utilization, infrastructure performance, scalability, and cloud costs
• Implement monitoring, logging, observability, and alerting for AI infrastructure and services
• Support distributed training and high-performance computing environments
• Build secure, highly available systems capable of supporting production AI workloads
• Partner with ML Engineers and Data Scientists to move models from experimentation into production
• Troubleshoot infrastructure, networking, performance, and deployment issues
• Evaluate emerging AI infrastructure technologies and recommend improvements to the platform

• 3+ years of experience in
  Cloud Infrastructure, Dev Ops, Platform Engineering, SRE, MLOps, or AI/ML Infrastructure
• Strong experience with at least one major cloud platform:
  AWS, Azure, or GCP
• Experience with
  Kubernetes and Docker
• Experience with Infrastructure-as-Code tools such as
  Terraform
• Strong scripting/programming skills in
  Python, Bash, Go, or similar languages
• Experience building CI/CD pipelines using tools such as Git Hub Actions, Git Lab CI, Jenkins, or similar
• Knowledge of networking, Linux systems, distributed computing, and cloud architecture
• Experience implementing monitoring and observability solutions
• Understanding of machine learning development and deployment workflows

• Experience managing
  GPU infrastructure
  , including NVIDIA GPUs and CUDA environments
• Experience with AI/ML frameworks such as
  PyTorch, Tensor Flow, JAX, or Hugging Face
• Experience supporting
  LLM training, fine-tuning, RAG, or inference workloads
• Experience with MLOps platforms such as
  MLflow, Kubeflow, Sage Maker, Vertex AI, or Azure Machine Learning
• Experience with distributed training technologies such as
  Ray, Deep Speed, PyTorch Distributed, or Horovod
• Familiarity with AI inference technologies such as
  vLLM, NVIDIA Triton, or TensorRT
• Experience managing Kubernetes-based GPU clusters
• Understanding of model serving, vector databases, and modern Generative AI architecture
• Experience optimizing infrastructure for performance and cloud/GPU cost efficiency

In this role, you will help create the infrastructure foundation that allows AI teams to
experiment faster, train models efficiently, deploy AI applications reliably, and scale them into production
. You will reduce friction between AI development and production while improving reliability, performance, security, and infrastructure cost.