AI Engineer

Allied Consultants•Austin, TX

14h•Onsite

About The Position

Texas GovLink, Inc. is an Austin-based firm which has been a leading provider of technical and business professionals to clients in Texas. Our client, the Texas Department of Transportation (TxDOT), is currently seeking a AI Engineer to join their Traffic Technology group to build safety-focused AI for Texas roads. This role will be for someone who has applied AI experience developing real world solutions and will focus on creating and enhancing operational AI models for video imaging, computer vision, and analytics. The successful candidate will bring the following: Real-world AI experience: Professional role or substantial internships where you worked on a team delivering AI solutions Hands-on background in some of: Model development (ideally computer vision / video analytics) Data/model pipelines (streaming or batch) UX/UI or front-end work focused on AI Comfortable working within an existing architecture and eager to learn and grow Local to Austin, TX and willing to be on-site 4–5 days/week At Texas GovLink, we value our consultants and are committed to providing an exceptional experience including: Highly competitive pay rates Local support staff for responsive, personal service Comprehensive benefits package, including: Medical insurance (with employer cost sharing) Life insurance A 401(K) plan with company match Flexible spending through a cafeteria plan Candidates selected for interviews will be subject to a criminal background check and may be required to pass a drug screening, in compliance with federal and state regulations. All offers of employment are contingent upon successful completion of these checks. Texas GovLink is a proud to be an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Requirements

3 Years Python – Production experience
3 AI/ML Production – Experience building and deploying ML models (1-3+) serving real users, not just experiments
3 Cloud Platforms – Experience with AWS, Azure, GCP, or OCI for deploying and managing ML workloads.
3 DevOps - Docker and Kubernetes experience
3 Databases - SQL (PostgreSQL, MySQL) and NoSQL/vector databases
3 Scripting - Proficient in both Bash and PowerShell for automation
3 Command Line Interface (CLI) – Production experience working in CLI terminal

Nice To Haves

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

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.
Act as liason 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.
Ensure automation projects comply with TxDOT’s IT governance, security, and audit requirements.
Promote reusable components and standardized AI development practices.
Conduct post-implementation reviews to capture lessons learned and improve delivery methods.
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.