AI Engineer with Transport or Traffic Expertise

AI Engineer

Location: Austin, TX - 4 days onsite ( LOCAL )

Duration: 12-month duration

Core Responsibilities:

• Gather and document AI solution requirements from business stakeholders.
• Develop AI proof-of-concepts and transition successful prototypes into production systems.
• Design and implement scalable AI pipelines for enterprise applications.
• Train, fine-tune, and validate AI/ML models for optimal performance.
• Write clean, efficient software code and scripts for AI workflows.
• Conduct rigorous testing and quality assurance of AI models and outputs.
• Ensure compliance with organizational IT governance, security, and audit standards.

Stakeholder Engagement & Communication

• Act as liaison between Traffic Technology team, business stakeholders, and automation developers.
• Facilitate requirements gathering and ensure clarity in AI solution design.
• Communicate progress, risks, and issues to project sponsors and leadership teams.

• .

Team Coordination & Support

• Collaborate with data engineers, business analysts, and infrastructure teams.
• Provide guidance on AI best practices and assist in troubleshooting.
• Support knowledge sharing and continuous improvement within the team.

Minimum Yrs of Experience, Skills, and Qualifications

• Python – 1-3+ years production experience, this is your primary language
• AI/ML Production - Built and deployed 1-3+ ML models serving real users, not just experiments
• Cloud Platforms - Experience with AWS, Azure, Google Cloud Platform, or OCI for deploying and managing ML workloads. We leverage AI/ML tools across all major cloud providers (Azure AI, AWS SageMaker/Bedrock, Google Cloud Platform Vertex AI, OCI AI Services)
• DevOps - Docker and Kubernetes experience
• Databases - SQL (PostgreSQL, MySQL) and NoSQL/vector databases
• Scripting - Proficient in both Bash and PowerShell for automation
• Command Line Interface (CLI) – 1-3+ years production experience working in CLI terminal.

Preferred Skills and Qualifications

• CI/CD Experience: Azure DevOps, GitHub Actions, Jenkins, or similar automation pipelines
• Computer Vision: Production CV experience with PyTorch/TensorFlow, OpenCV, object detection, segmentation, or real-time inference
• Additional Languages: Go or Rust experience for performance-critical components
• Feature stores (Feast, Tecton) or advanced feature engineering
• Model optimization: quantization, pruning, knowledge distillation
• Edge deployment or resource-constrained model deployment
• Experiment frameworks for A/B testing ML models
• Contributions to open-source ML projects
• Real-time streaming data processing (Kafka, Kinesis)