Tech – Software Engineer: AI-Assisted Incident Response & RCA

Important

Interested in this project? When applying, remember to indicate the Project Title as listed above in the application form.

Role

Software Engineer

Division

Operations Engineering Practice

Level

Undergraduate

Internship Duration

6 months, 3 months

Project Description

Interested in this project?

Submit your application and indicate this project as your preference.

Apply Now!

Back to Software Engineer listings

AI-Assisted Incident Response & Root Cause Analysis (RCA) GTO Operations Engineering Practice (OEP) is exploring how Agentic AI tools can help engineers investigate and mitigate production incidents more effectively. This project will research emerging AIOps and Agentic AI approaches, assess how AI can be applied to operational data such as logs, metrics, traces, code and runbooks, and build a prototype that can assist engineers in identifying root causes and recommending mitigation actions. The project will evaluate the prototype against representative incident scenarios, with the aim of reducing the time required to diagnose and mitigate incidents.

Learning Outcomes

- AIOps & Agentic AI: Gain practical understanding of how LLMs and Agentic AI can be applied to production operations, incident investigation and mitigation. - Observability & Incident Management: Learn to use logs, metrics, traces and other operational signals to troubleshoot systems and identify root causes. - AI Agent Development: Gain hands-on experience building AI agents that can interact with engineering tools, operational data and runbooks to investigate incidents. - Systems & Reliability Engineering: Develop an understanding of common production failure modes, troubleshooting techniques and Site Reliability Engineering (SRE) practices. - Experimentation & Evaluation: Learn to design incident scenarios and evaluate the effectiveness, accuracy and safety of AI-assisted incident response.

Prerequisites

- Proficient in at least one programming language, such as Python, Go, TypeScript or JavaScript. - Comfortable working with Linux, command-line tools and basic scripting. - Familiarity with software development and DevOps concepts such as Git, CI/CD and APIs. - Basic understanding of cloud infrastructure, containers or distributed systems. - Familiarity with Large Language Models (LLMs), AI coding tools or observability platforms will be an advantage but is not required.