What are the responsibilities and job description for the Artificial Intelligence/Machine Learning Engineer Intern position at 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.
• 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
• 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
• 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
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
• 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
• 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