The workers responsibilities and skills must include:
The AI Apprenticeship Program establishes a sustainable and governed pathway for developing entry level AI talent in support of AI Strategic Plan.
Under supervision, the intern will:
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
Level & Experience Alignment
Level 2 (Intern / Apprentice Equivalent):
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):
o Data pipelines (e.g., Airflow, Prefect, or cloud native equivalents)
o Model deployment concepts (e.g., REST APIs, serverless patterns)
o Cloud platforms or AI services (AWS, Azure, Google Cloud Platform, OCI)
o Containerization concepts (Docker)
o CI/CD fundamentals
o Monitoring or model versioning concepts
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