AI Engineer

The AI Engineer will develop AI/ML proof-of-concepts and build scalable AI solutions that integrate with TxDOT’s operational technology workflows.

Embedded within the Traffic Technology team, this role will support efforts to enhance roadway safety and operational efficiency through AI-driven innovation.

The engineer will collaborate with cross-functional groups—including the ITD AI team and TRF—to gather requirements, develop AI software, and deliver validated solutions to end-users.

• Gather, document, and refine AI solution requirements from business stakeholders.
• Develop AI/ML proof-of-concepts and transition successful prototypes into production environments.
• Design and implement scalable, production-ready AI/ML pipelines for enterprise systems.
• Train, fine-tune, and validate machine learning models to ensure reliability and performance.
• Write clean, efficient, and maintainable software and scripts to support AI workflows.
• Conduct comprehensive testing and quality assurance for AI models and outputs.
• Ensure all solutions adhere to IT governance, security, and audit standards.

• Serve as a liaison between Traffic Technology, business stakeholders, developers, and AI teams.
• Facilitate requirements gathering and clarify solution design considerations.
• Provide consistent updates on project progress, risks, and issues to leadership teams.

• Ensure AI solutions comply with TxDOT IT governance, security, and audit frameworks.
• Promote standardized AI development practices and reusable components.
• Conduct post-implementation reviews and contribute to continuous improvement initiatives.

• Collaborate with data engineers, business analysts, and infrastructure teams on model deployment and data workflows.
• Provide guidance on AI/ML best practices and troubleshoot technical issues.
• Participate in knowledge sharing and contribute to team skill development.

• 1–3+ years of production experience with Python as the primary development language.
• Experience building and deploying 1–3+ ML models used by real end-users.
• Hands-on experience with at least one major cloud platform (AWS, Azure, GCP, or OCI) for deploying ML workloads.
• Experience with Dev Ops tools including Docker and Kubernetes.
• Proficiency with SQL and No
  
  SQL/vector databases.
• Strong scripting skills in Bash and Power Shell for automation tasks.
• 1–3+ years of experience working in command-line interfaces for production workflows.

• Experience with CI/CD tools such as Azure Dev Ops, Git Hub Actions, or Jenkins.
• Production experience in computer vision using frameworks such as PyTorch, Tensor Flow, or OpenCV.
• Experience with performance-focused programming languages such as Go or Rust.
• Experience with feature stores (e.g., Feast, Tecton) or advanced feature engineering.
• Familiarity with model optimization techniques such as pruning, quantization, or knowledge distillation.
• Experience with deploying models on edge or resource-constrained devices.
• Understanding of A/B testing frameworks for model experimentation.
• Contributions to open-source ML projects.
• Experience with real-time streaming data tools such as Kafka or Kinesis.