Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Infojini
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
Source: Infojini careers