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DirectClient: Texas Department of Transportation(TXDOT)
Solicitation Number: SCTASK1572203
Title: AI/ML Intern
Location: 6230 E Stassney Ln, Austin, TX 78744
Duration: 12 months with possible extension
Last date for submission: April 20, 2026 (2.00 PM-CST)
Important Note: This position requires candidates to be in onsite 5 days a week. Texas local candidates only.
DESCRIPTION OF SERVICES:
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:
DirectClient: Texas Department of Transportation(TXDOT)
Solicitation Number: SCTASK1572203
Title: AI/ML Intern
Location: 6230 E Stassney Ln, Austin, TX 78744
Duration: 12 months with possible extension
Last date for submission: April 20, 2026 (2.00 PM-CST)
Important Note: This position requires candidates to be in onsite 5 days a week. Texas local candidates only.
DESCRIPTION OF SERVICES:
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