Junior AI/ML Engineer

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