Role: 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, GCP, 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, GCP, 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