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AI Apprenticeship Intern

TexcelVision
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The workers responsibilities and skills must include:

The AI Apprenticeship Program establishes a sustainable and governed pathway for developing entry level AI talent 

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, GCP, OCI)
o Containerization concepts (Docker)
o CI/CD fundamentals
o Monitoring or model versioning concepts