Artificial Intelligence/Machine Learning Engineer Intern

Job Description:

The AI Apprenticeship Program establishes a sustainable and governed pathway for developing entry-level AI talent in support of AI Strategic Plan. Under supervision.

Responsibilities:

Assist in evaluating emerging AI trends, tools, and vendor solutions against defined business use cases Support proof of concept (PoC) efforts to assess feasibility, data readiness, and potential value Contribute to the development of AI/ML models and prototype applications for prioritized use cases Help design and document data and AI pipelines that integrate with existing systems Create reports, analyses, and presentations that communicate findings and outcomes clearly Collaborate with data, engineering, software development, and governance teams Minimum Yrs of Experience, Skills, and Qualifications Typically 1-3 years of academic, internship, or entry-level experience in AI, data science, software engineering, or a related field Possesses foundational knowledge of common concepts, tools, and practices Works under guidance using established processes and standards Does not typically exercise independent production decision-making Minimum Qualifications, Skills, and Experience Education / Learning Background Coursework toward or completion of a degree in Computer Science, Data Science, Engineering, Mathematics, or related discipline Demonstrated interest in artificial intelligence, machine learning, and applied analytics Technical Skills (Foundational / Developing) Proficiency in Python Familiarity with object-oriented programming concepts Experience with version control (Git) Exposure to data processing, analysis, and basic model development Understanding of basic software development and testing concepts

Preferred Skills and Qualifications:

Familiarity with one or more of the following (hands-on or academic): Data pipelines (e.g., Airflow, Prefect, or cloud-native equivalents) Model deployment concepts (e.g., REST APIs, serverless patterns) Cloud platforms or AI services (AWS, Azure, Google Cloud Platform, OCI) Containerization concepts (Docker) CI/CD fundamentals Monitoring or model versioning concepts