Intern

Job Description Summary

As an Intern- AI and Data Science for MICT team, you will lead the development of an intelligent system capable of automating root cause analysis (RCA) from historical service record data. This role involves designing and deploying a scalable AI model that can process unstructured complaint text data and extract actionable insights such as root causes, resolutions, and parts replaced across a wide range of product categories.

GE HealthCare is a leading global medical technology and digital solutions innovator. Our purpose is to create a world where healthcare has no limits. Unlock your ambition, turn ideas into world-changing realities, and join an organization where every voice makes a difference, and every difference builds a healthier world. For more information visit: https://www.gehealthcare.in/

Roles and Responsibilities

- We are looking for a passionate and highly motivated individual, who has some knowledge on Large Language Models (LLMs)/ Transformers/ GenAI/ Agentic AI, to join our MICT team at GE Healthcare, to do internship.

- As an intern, you are expected to develop and deploy an AI model to analyse closed service records spanning across various product configurations in Install Base.

- Extract and organize critical information such as root causes, resolutions, and relevant keywords to represent the distribution of contributing factors, enabling a comprehensive and holistic analysis.

- Ensure high accuracy and reliability of model outputs with possible auto-validation, minimizing hallucinations and manual revalidation, involving large- scale datasets.

- Implement robust data preprocessing pipelines to clean, normalize, and structure service record data. • Evaluate and fine-tune Large Language Models (LLMs) such as GPT-4o for domain specific RCA tasks.

- Design and integrate a Retrieval-Augmented Generation (RAG) framework to enhance model responses using historical records and technical documentation.

- Collaborate with domain experts to validate model outputs and incorporate feedback for continuous improvement.

- Explore and execute model training strategies and agent introduction if pre-trained LLMs do not meet performance expectations.

- Work with cloud platforms and tools (e.g., AWS) and leverage Python for model development and deployment.