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We are seeking an Artificial Intelligence Intern for Spring 2026 to join our innovative team and support the development of advanced AI and machine learning solutions. This internship will focus on enhancing automation, analytics, and business intelligence across the organization. The ideal candidate will apply both traditional machine learning techniques (such as clustering, regression, and classification) and cutting-edge large language model (LLM) approaches—including prompt engineering, retrieval-augmented generation (RAG), and parameter-efficient fine-tuning—to optimize processes and deliver strategic insights for HealthLink Dimensions.
Job Responsibility:
Develop and implement machine learning and deep learning solutions to improve automation, data analysis, and decision support for HealthLink Dimensions’ products and services
Build, test, and optimize prompt-engineered applications, RAG pipelines, and other LLM-based systems tailored to real-world business scenarios
Develop and maintain robust data pipelines supporting machine learning workflows, including data collection, cleaning, transformation, and integration from multiple healthcare data sources
Implement evaluation strategies to measure the performance of models and ensure outputs align with business objectives and compliance standards
Collaborate with cross-functional teams—including engineering, data, and business stakeholders—to identify AI use cases that can drive measurable value
Document development processes, methodologies, and findings in clear, reproducible formats suitable for both technical and non-technical audiences
Perform additional research, development, and analytical tasks as required to support innovation within HealthLink Dimensions’ AI initiatives
Requirements:
Current Master’s student in a relevant field such as Computer Science, Statistics, or Data Science
Strong programming skills in Python
Experience using major AI/ML libraries, including scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers, LangChain, and OpenAI API
Proven experience developing machine learning pipelines from data preprocessing through deployment and evaluation
Hands-on experience applying and fine-tuning Large Language Models (LLMs) to solve complex data challenges
Ability to analyze data and model quality, extract actionable insights, and communicate findings effectively
Experience with data preprocessing, feature engineering, and handling structured and unstructured data from diverse sources
Familiarity with vector databases and RAG pipelines
Basic understanding of SQL
Strong statistical foundation for deriving insights from large-scale data
Excellent problem-solving skills and keen attention to detail
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