Artificial Intelligence/Machine Learning Engineer 2 – Internship Program

TxDOT work to be accomplished
The AI Apprenticeship Program establishes a sustainable and governed pathway for developing entry-level AI talent in support of TxDOT's AI Strategic Plan. Under supervision, the intern will:
• Assist in evaluating emerging AI trends, tools, and vendor solutions against defined business use cases
• 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
An AI Apprentice will also provide direct support for Planning & Administration team in the following areas:
• Assisting TPOs in identifying and assessing AI opportunities across supported divisions.
• Creating proofs of concept (POCs) and pilots for AI and automation use cases.
• Researching, maintaining documentation, analyzing findings, and preparing reports for AI initiatives.
• Supporting AI governance activities, developing risk assessments, and maintaining compliance documentation.
• Assessing vendor AI capabilities and emerging AI technologies.
• Maintaining and supporting AI applications developed for the divisions.
• Contributing to AI modernization initiatives in divisions such as AVN, MRD, RTI, RRD, and other business areas exploring automation opportunities.
• Serving as Administrators for enterprise applications.
Minimum Yrs of Experience, Skills, and Qualifications
Minimum Years 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
• Exposure to data analysis, data migration, and basic model development
• Understanding of basic software development and testing concepts

(Exposure or Academic Experience is Acceptable)
• 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
Skills and Qualifications
(Exposure or Academic Experience is Acceptable)
• 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

Key Attributes for Success:
• Strong analytical and problem-solving skills
• Ability to communicate technical concepts clearly to non-technical audiences
• Willingness to learn and adapt in a fast-evolving technical environment
• Attention to detail and commitment to data quality
• Collaborative mindset and openness to mentorship and feedback