What are the responsibilities and job description for the AI/ML Engineer II - Intern position at Jobs via Dice?
City : Austin
State : Texas
Neos is Seeking an AI/ML Engineer II - Intern for a contract role with our client in Austin, TX.
No calls, no emails, please respond directly to the "apply" link with your resume and contact details.
Work to be accomplished
The AI Innovation team in ITD undertakes rapid development initiatives using AI/ML tools and platforms with the goal to drive business innovation at the Texas Department of Transportation.
Possesses raw knowledge and skillset through coursework with some hands-on work experience in the following areas:
State : Texas
Neos is Seeking an AI/ML Engineer II - Intern for a contract role with our client in Austin, TX.
- ONSITE - ONLY CANDIDATES CURRENTLY RESIDING IN THE AUSTIN, TX AREA (within 50 miles) WILL BE CONSIDERED***
No calls, no emails, please respond directly to the "apply" link with your resume and contact details.
Work to be accomplished
The AI Innovation team in ITD undertakes rapid development initiatives using AI/ML tools and platforms with the goal to drive business innovation at the Texas Department of Transportation.
- Evaluate emerging AI trends, tools, and vendor solutions against business use-cases.
- Run proof-of-concepts (PoCs) to test feasibility of new ideas.
- Design and build applications and AI/ML models tailored to specific use cases (e.g., predictive analytics, natural language processing, computer vision) prioritized for the AI Program.
- Create scalable AI pipelines that can be integrated into existing systems.
- Collaborate with data, engineering, and software development teams.
Possesses raw knowledge and skillset through coursework with some hands-on work experience in the following areas:
- Strong Python, familiarity with Java / C / Go for production environments
- Object-oriented programming & design patterns
- Unit testing, CI/CD, Git, containerization (Docker)
- Data pipelines (Airflow, Prefect, or cloud-native equivalents)
- Model deployment (REST APIs, gRPC, serverless), monitoring, and versioning
- AWS / Azure / Google Cloud Platform / OCI AI services
- Cloud-native training/inference environments (SageMaker, Vertex AI, Azure ML)
- Kubernetes for scalable inference