AI Infrastructure & Platform Engineering Intern

AI Infrastructure & Platform Engineering Intern

Location: Lakeland, FL (On-site)

Employment Type: Internship

About the Role

Aconcagua.ai is building a next-generation GPU cloud platform for AI training, inference, and enterprise workloads. We are seeking an AI Infrastructure & Platform Engineering Intern to help build and operate the infrastructure powering our NVIDIA GPU cloud. This role offers hands-on experience across GPU systems, Kubernetes, cloud infrastructure, platform engineering, and AI workloads. You will work closely with senior engineers while contributing to real-world infrastructure, automation, and platform development projects.

Responsibilities

• Assist in operating and maintaining NVIDIA GPU infrastructure and Kubernetes clusters.
• Contribute to platform services, APIs, and automation tools used by customers and internal teams.
• Support deployment, testing, and optimization of AI training and inference workloads.
• Help build monitoring, observability, and operational tooling for infrastructure and applications.
• Participate in infrastructure automation, CI/CD pipelines, and cloud operations.
• Troubleshoot system, application, and infrastructure issues with guidance from senior engineers.
• Support security, networking, storage, and platform reliability initiatives.
• Collaborate with engineering teams on projects that improve scalability, performance, and operational efficiency.

Minimum Qualifications

• Pursuing or recently completed a Master’s/bachelor’s degree in computer science, Computer Engineering, Information Technology, Electrical Engineering, or a related technical field.
• Strong understanding of Linux fundamentals and command-line tools.
• Programming experience with Python, Go, or similar languages.
• Familiarity with Git, GitHub, containers (Docker), and software development fundamentals.
• Good understanding of cloud computing, networking, and Kubernetes concepts. Strong problem-solving, debugging, and communication skills.
• Demonstrated passion for infrastructure, cloud platforms, AI systems, or distributed systems through personal projects, coursework, open-source contributions, or technical communities.
• Strong GitHub portfolio showcasing relevant projects is highly preferred.

Preferred Qualifications

• Hands-on projects involving Linux systems, cloud infrastructure, Kubernetes, or DevOps.
• Experience with AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, or LangChain.
• Familiarity with NVIDIA GPUs, CUDA, GPU computing concepts, or AI infrastructure.
• Exposure to Kubernetes, Terraform, Ansible, CI/CD pipelines, or Infrastructure-as-Code.
• Experience with monitoring and observability tools such as Prometheus or Grafana.
• Familiarity with databases, object storage, APIs, or distributed systems.
• Contributions to open-source projects or technical communities.
• Personal, academic, or hackathon projects demonstrating strong engineering fundamentals.

Portfolio & Project Requirements

Candidates should provide at least one of the following:

• Active GitHub profile with relevant infrastructure, cloud, DevOps, AI, or software engineering projects.
• Personal portfolio showcasing technical projects and engineering work.
• Open-source contributions.
• Academic, research, or hackathon projects demonstrating practical problem-solving skills.
• Technical blog posts, demos, or project documentation showcasing engineering knowledge.

Preferred Certifications

Candidates holding one or more of the following certifications or equivalent training are highly preferred:

• Certified Kubernetes Administrator (CKA)
• NVIDIA Deep Learning Institute (DLI) Certifications
• AWS Cloud Practitioner or AWS Certified Solutions Architect – Associate

Pay: $20.00 - $25.00 per hour

Work Location: In person