AI Infrastructure Specialist | MLOps & GPU Cloud Role

EdgeQ Technologies Private Limited; United States; Remote; USD 95,000 - 155,000; Full time

Benefits offered by this job

100% remote

Health insurance

Professional development support

Certification reimbursement

Paid time off

Job summary

EdgeQ Technologies Private Limited seeks an AI Infrastructure Specialist to design, build, deploy, and maintain scalable AI/ML infrastructure across major cloud providers.

This fully remote role requires hands-on Kubernetes, GPU computing, and MLOps expertise; you will optimize performance, support deployment pipelines, and collaborate with AI/ML engineers and DevOps teams.

Qualifications

• 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.

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 CI/CD.
• 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.

Skills

GPU computing

Kubernetes

MLOps pipelines

Triton/vLLM

Terraform/Ansible

Cloud platforms

CI/CD

Python scripting

Education

Bachelor's degree in CS/IT/Engineering

Tools

Docker

Kubeflow

MLflow

Ray

NVIDIA Triton

Prometheus/Grafana

Job description

Location Remote

Company EdgeQ Technologies Private Limited

Market Positioning: The $95K–$155K range is well-aligned with current market rates for AI/MLOps infrastructure roles in the US. Mid-level cloud/DevOps engineers in AI typically earn $110K–$140K, so the ceiling here is competitive for 4–6 years of experience. Junior candidates (2–3 years) may find the floor reasonable but not exceptional.

Skills in High Demand: This role sits at the intersection of three of the hottest areas in tech — AI/ML, cloud infrastructure, and DevOps. Skills you'll build and deepen include:

• GPU cluster management and CUDA optimization (NVIDIA stack)
• MLOps pipelines with Kubeflow, MLflow, or Ray
• LLM deployment and inference optimization (vLLM, Triton) — a rapidly growing niche
• Kubernetes at scale for AI workloads
• IaC with Terraform/Ansible

Career Trajectory: This role is a strong stepping stone toward Senior ML Platform Engineer , AI Infrastructure Architect, or Head of MLOps — all roles commanding $160K–$220K+ at top tech companies.

Green Flags: The breadth of tech stack (multi-cloud, MLOps, LLMs) means you'll gain diverse, transferable experience. The mention of certification support is a genuine career accelerant.

Yellow Flags: The company (EdgeQ Technologies Private Limited) appears to be a smaller firm; growth stage and team size are not disclosed. Research the company's funding, headcount, and product roadmap before accepting. "Where applicable" on retirement benefits is worth clarifying.

Job Title: AI Infrastructure Specialist

Location: United States (Remote)

Work Mode: 100% Remote

Experience: 2–6 Years

Salary Range: $95,000 – $155,000 per year (USD)

Employment Type: Full-Time

Job Category: Artificial Intelligence / Cloud Infrastructure / DevOps

We are seeking a skilled and experienced AI Infrastructure Specialist to join our growing technology team in the United States. The ideal candidate will be responsible for designing, building, deploying, and maintaining scalable infrastructure to support AI and machine learning workloads.

The candidate should have strong knowledge of cloud infrastructure, GPU computing, containerization, Kubernetes, MLOps, and AI model deployment. This role involves optimizing infrastructure performance, ensuring scalability, and supporting reliable AI-powered applications.

This position offers a fully remote work opportunity for qualified candidates across the United States.

Key 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.

Required Skills & Qualifications

• 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.

Salary & Benefits

• Competitive compensation based on experience and technical expertise.
• 100% remote work opportunity.
• Flexible working arrangements.
• Health, dental, and vision insurance.
• Paid time off and holidays.
• Retirement savings benefits, where applicable.
• Professional development and technical certification support.

Work Location

United States – Fully Remote

• Candidates from eligible locations across the United States are encouraged to apply.
• Must be legally authorized to work in the United States.
• Ability to collaborate with distributed engineering teams across different US time zones.

Equal Opportunity Employer: We welcome qualified candidates from diverse professional backgrounds and encourage applications from eligible candidates across the United States.

Is this AI Infrastructure Specialist role fully remote? Yes, the position is 100% remote. Candidates must be legally authorized to work in the United States and be able to collaborate across US time zones. What is the salary range for this position? The annual salary range is $95,000 to $155,000 USD, based on experience and technical expertise. What cloud platforms and tools are required for this role? Candidates should have strong experience with AWS, Azure, or Google Cloud, along with Docker, Kubernetes, Terraform, and monitoring tools like Prometheus, Grafana, or Datadog. Do I need a degree to apply for this position? A Bachelor's or Master's degree in Computer Science, IT, or Engineering is listed as preferred, not required. Relevant experience in AI infrastructure, DevOps, or cloud roles is the primary qualification. Does this job offer professional development support? Yes, the role includes professional development and technical certification support as part of the benefits package.