Job Description

As an AI Engineer / Data Science Specialist, you will be part of an AI-enabled Systems Engineering organization focused on transforming healthcare product development through Artificial Intelligence, Machine Learning, Data Science, and Generative AI technologies.
You will work on large-scale engineering, quality, and product lifecycle datasets to develop intelligent solutions that improve engineering productivity, product quality, regulatory compliance, and patient outcomes. Working alongside Systems Engineers, AI Scientists, Software Engineers, Verification & Validation Engineers, and Domain Experts, you will help build the next generation of AI-powered engineering workflows and healthcare technologies.
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 helps build a healthier world.

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Roles & Responsibilities

• Develop AI, machine learning, and analytics solutions to address engineering and business challenges.
• Collaborate with Systems Engineers, Software Engineers, and Verification Engineers to create AI-driven engineering productivity tools and workflows.
• Analyze structured and unstructured datasets from requirements, defects, test results, quality systems, documentation, and operational processes.
• Perform exploratory data analysis, statistical analysis, and feature engineering on complex datasets.
• Build, evaluate, and deploy machine learning models for predictive, descriptive, and generative AI use cases.
• Develop Retrieval-Augmented Generation (RAG), Agentic AI, and LLM-based applications for engineering knowledge management and workflow automation.
• Participate in data preparation, cleansing, validation, and quality assessment activities.
• Create dashboards, visualizations, reports, and technical documentation to communicate findings and model performance.
• Contribute to responsible AI practices, model governance, traceability, and validation processes.
• Stay current with emerging AI technologies and identify opportunities to apply them to healthcare engineering problems.
• Support prototype development, experimentation, and proof-of-concept activities for future AI-enabled engineering capabilities.

Required Qualifications

• Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering, or another STEM discipline.
• 0–2 years of experience in AI, Data Science, Software Engineering, Analytics, or related fields.
• Strong programming skills in Python.
• Proficiency in Python and common data science, machine learning, and AI frameworks (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, or equivalent).
• Exposure to Generative AI and LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, OpenAI / Azure OpenAI APIs
• Understanding of:
  • Machine Learning fundamentals
  • Statistics and probability
  • Data structures and algorithms
  • Model evaluation techniques
• Knowledge of SQL and data manipulation techniques.
• Strong analytical and problem-solving skills.
• Effective written and verbal communication skills.
• Ability to work in a collaborative, cross-functional environment.
• Passion for innovation, continuous learning, and solving real-world problems using AI.

Preferred Qualifications

• Internship or project experience in AI, Machine Learning, Data Science, or Software Development.
• Knowledge of Generative AI, Large Language Models (LLMs), Prompt Engineering, and Agentic AI concepts.
• Familiarity with cloud platforms such as Azure, AWS, or GCP.
• Experience with version control (Git) and software development practices.
• Exposure to healthcare, medical devices, quality systems, or regulated environments.
• Experience working with large-scale structured and unstructured data.
• Familiarity with data visualization tools such as Power BI, Tableau, or equivalent.