AI/ML Engineer

Role: Artificial Intelligence/Machine Learning Engineer (AI/ML Engineer)

Duration: 12 Months

Location: Austin, TX 78744 (This role requires onsite presence 4–5 days per week. Candidates must reside in the Austin area and be authorized to work in the U.S.

Experience: 3+ Years

Summary:

The AI Apprenticeship Program establishes a sustainable and governed pathway for developing entry-level AI talent in support of the client’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

•    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

Preferred (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

Preferred Skills and Qualifications

Preferred (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