AIOps Forward Deployment Engineer Intern, BS - Summer 2027

Marvell Technology•Santa Clara, CA

13d•$32 - $65

About The Position

Marvell’s Employee Experience and Digital Workplace team builds and supports digital tools and experiences that help employees collaborate, find information, and work effectively. The team manages workplace platforms, promotes adoption through training and communication, and uses employee feedback to improve the experience. This internship focuses on AI-assisted IT support and automation. Working with a mentor, the intern will contribute to a focused project to help detect issues earlier, gather troubleshooting evidence, or reduce repetitive operational work. The intern will build and test a small script, workflow, or AI-powered prototype, explore approved sample tickets and other information, evaluate AIOps tools and approaches, and present the project and lessons learned to the team. Work will follow privacy and security guidance, use a test environment, and receive engineering review for production changes. The position is intended for a student currently pursuing a bachelor’s degree in a relevant field, with an expected graduation date between Fall 2027 and Summer 2028. Previous internships, professional AIOps experience, production deployment experience, and familiarity with Marvell’s internal platforms are not required. The expected base pay is $32–$65 per hour. Intern benefits include medical, dental, and vision coverage, perks and discounts, mental health resources, and paid holidays. Additional compensation may be available for intern PhD candidates. Candidates must be eligible to access export-controlled information under applicable law. Some applicants may be subject to an export license review process prior to employment. AI tools may not be used during interviews unless instructed by the interviewer.

Requirements

Currently pursuing a bachelor’s degree in computer science, computer engineering, information systems, data science, AI/ML, or a related field, with an expected graduation date between Fall 2027 and Summer 2028.
Passion for AIOps and interest in applying AI and automation to IT operations.
Knowledge of current AIOps tools, approaches, and use cases, and ability to discuss examples explored through reading, coursework, technical communities, or personal projects.
Ability to write and debug basic code in Python, JavaScript, PowerShell, or a similar language; coursework and personal projects count.
Basic familiarity with REST APIs, JSON, and Git, including following documentation to retrieve sample data, inspect a response, and track code changes.
Hands-on experience with at least one small AI or automation project through coursework, a personal project, or a hackathon, and ability to explain the problem, personal contribution, and how results were checked.
Foundational understanding of logs, metrics, and alerts, and basic device or network troubleshooting concepts such as CPU usage, latency, and packet loss.
Basic understanding of how large language models use prompts and retrieved information, including why a convincing answer may still be incorrect.
Willingness to handle operational data responsibly, protect credentials, use approved tools, and seek guidance before an automation takes action.
Curiosity, willingness to ask questions and try unfamiliar tools, and openness to feedback.
Ability to explain work clearly and collaborate with technical and nontechnical teammates.
Carefulness and attention to detail, including checking results rather than assuming an AI-generated answer is correct.

Responsibilities

Work with a mentor and IT teammates to identify a practical problem that AI or automation could help solve.
Build a small script, workflow, or AI-powered prototype with guidance from experienced engineers.
Explore approved sample tickets, alerts, or knowledge articles to find useful patterns and troubleshooting information.
Try relevant AIOps tools and ideas and share findings with the team.
Help connect approved device-health, meeting-quality, or support signals through APIs and bring related information together for troubleshooting.
Test simple rules for grouping repeated alerts or identifying unusual patterns, and review missed issues and false alarms with a mentor.
Improve an AI-assisted troubleshooting workflow by checking answers against technical evidence, documenting gaps, and proposing changes for engineer review.
Test the prototype, assess the usefulness and accuracy of AI-generated answers, and improve it based on feedback.
Follow privacy and security guidance, work in a test environment, and ensure engineers review any production changes.
Present the project to the team in a practical demonstration, including results and lessons learned.