AI Solution Architect Intern

Job Description

Intel is seeking an AI Infrastructure Solution Architect Intern to join our AI Platforms team. As an intern, you will work alongside experienced engineers to support AI accelerator platforms, cluster deployments, system validation, and AI workload benchmarking. This role provides hands-on experience across AI infrastructure, system software, cluster operations, firmware debugging, and end-to-end AI solution development.

The ideal candidate is passionate about AI systems, computer architecture, Linux, and large-scale computing environments, and enjoys solving complex technical problems.

Responsibilities

AI Infrastructure and Cluster Operations

• Assist in deploying and validating AI accelerator systems and multi-node AI clusters.
• Support cluster bring-up activities, including system provisioning, software installation, and health verification.
• Execute performance benchmarks and collect system telemetry.
• Help troubleshoot system, networking, and software issues in AI environments.

System Debugging and Validation

• Analyze hardware, firmware, driver, and software logs to identify failures.
• Assist with validation of BIOS, firmware, and system software configurations.
• Support development of test plans and automated validation workflows.
• Participate in root-cause investigations of system performance and stability issues.

AI/ML Workloads

• Deploy and evaluate AI models using frameworks such as PyTorch and vLLM.
• Benchmark AI workloads and identify performance bottlenecks.
• Assist in reproducing customer-reported issues in lab environments.
• Contribute to optimization studies for AI inference and training workloads.

Automation and Tool Development

• Develop Python-based tools to automate testing, monitoring, and data collection.
• Create dashboards, scripts, and utilities for cluster management and diagnostics.
• Contribute to documentation, runbooks, and technical knowledge bases.

End-to-End AI Solutions

• Participate in building end-to-end AI solutions, such as generative AI, agentic AI, computer vision, robotics, or intelligent automation projects.
• Work with cross-functional teams to integrate hardware, software, AI models, and applications into complete solutions.

Qualifications

Minimum Qualifications

• Currently pursuing a Bachelor's degree in Computer Science, Computer Engineering, Electrical Engineering, Data Science, AI/ML, or related fields.
• Experience with Python programming.
• Familiarity with Linux command line and system administration concepts.
• Understanding of computer architecture, operating systems, networking, or distributed systems.
• Demonstrated ability to solve complex engineering or technical problems through coursework, research, personal projects, robotics competitions, hackathons, open-source contributions, or previous internships.
• Strong analytical and problem-solving skills.
• Ability to work independently and collaborate in a team environment.

Preferred Qualifications

Technical Skills:

• Experience with Python, C/C++, or Bash scripting.
• Linux/Unix environments.
• Git and software development workflows.
• Docker or containerized applications.

Familiarity with:

• PyTorch, TensorFlow, or other AI frameworks.
• LLMs, RAG, agentic AI, or generative AI applications.
• Cloud or cluster computing environments.
• GPU or AI accelerator technologies.

Project Experience:

• Coursework, research, personal projects, or internships involving AI/ML applications, robotics systems, autonomous systems, computer vision, distributed computing, high-performance computing (HPC), or end-to-end AI solution development.

Soft Skills:

• Strong communication and presentation skills.
• Curiosity and willingness to learn new technologies.
• Ability to diagnose problems systematically and communicate findings clearly.

What You Will Learn

• AI accelerator and cluster architecture.
• AI infrastructure deployment and operations.
• System-level debugging and performance optimization.
• AI model deployment and benchmarking.
• Automation, monitoring, and validation frameworks.
• Customer-facing AI solution development and deployment best practices.