США; Candidates from eligible locations across the United States are encouraged to apply; Ability to collaborate with distributed engineering teams across different US time zones; We welcome qualified candidates from diverse professional backgrounds and encourage applications from eligible candidates across the United States
Responsibilities:
— Design, deploy, and maintain scalable infrastructure for AI and machine learning applications.
— Manage cloud-based AI infrastructure across AWS, Microsoft Azure, or Google Cloud.
— Configure and optimize GPU-based computing environments for AI workloads.
— Implement and maintain Kubernetes clusters and Docker containers.
— Support AI model training, deployment, and inference environments.
— Build and maintain MLOps pipelines for continuous integration and deployment.
— Monitor infrastructure performance, availability, scalability, and resource utilization.
— Optimize GPU, CPU, memory, and storage resources for AI workloads.
— Implement infrastructure automation using Terraform, Ansible, or similar tools.
— Collaborate with AI engineers, ML engineers, and DevOps teams.
— Implement infrastructure security, access controls, and monitoring solutions.
— Troubleshoot infrastructure issues and ensure system reliability.
— Maintain technical documentation and infrastructure best practices.
Requirements:
— 2–6 years of experience in AI Infrastructure, Cloud Infrastructure, DevOps, or a related field.
— Strong knowledge of AWS, Azure, or Google Cloud platforms.
— Hands-on experience with Docker and Kubernetes.
— Understanding of GPU computing and AI workload optimization.
— Experience with Linux administration and system troubleshooting.
— Knowledge of Python, Bash, or Shell scripting.
— Familiarity with machine learning model deployment and inference workflows.
— Experience with CI/CD pipelines and infrastructure automation.
— Knowledge of Terraform, Ansible, or similar infrastructure tools.
— Understanding of networking, storage, and cloud security fundamentals.
— Experience with monitoring tools such as Prometheus, Grafana, or Datadog.
— Strong analytical, troubleshooting, and problem-solving skills.
Preferred Qualifications:
— Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
— Experience with NVIDIA GPUs, CUDA, and GPU resource management.
— Familiarity with MLflow, Kubeflow, Ray, or similar MLOps frameworks.
— Knowledge of LLM deployment and inference optimization.
— Experience with distributed computing and high-performance computing (HPC).
— Familiarity with serverless AI infrastructure and microservices architecture.
— Understanding of model serving frameworks such as NVIDIA Triton, vLLM, or TorchServe.
— Experience managing large-scale AI infrastructure environments.
Benefits:
— Competitive annual salary of $95,000 – $155,000 USD.
— 100% remote work opportunity within the United States.
— Flexible working arrangements.
— Health, dental, and vision insurance.
— Paid time off and holidays.
— Retirement savings benefits, where applicable.
— Professional development and technical certification support.
— Career advancement opportunities.
Candidates must be legally authorized to work in the United States and able to collaborate with distributed engineering teams across different US time zones.
Зарплаты и спрос: Инфраструктура и безопасность
Сейчас в каталоге 4 170 вакансий по этому направлению, удалённо можно работать в 31%.
Середина вилки в этой вакансии, 125 000 $ в год, на 33% ниже медианы по направлению (186 000 $). У половины вакансий от 140 000 $ до 240 000 $ (вакансий с вилкой: 210).
Навыки этой вакансии, которые чаще всего просят по направлению: Linux (19%), Grafana (18%), Kubernetes (15%), Python (14%), Docker (11%), Ansible (10%), Bash (10%), Terraform (9%). В скобках доля вакансий направления.
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