AI Engineer I

Compunnel, Inc.; Austin (TX); On-site; USD 80,000 - 120,000; Full time

Job summary

A leading tech firm in Austin, Texas, is seeking an AI Engineer to develop and deploy AI/ML solutions integrating with operational workflows. The role involves collaborating with cross-functional teams to enhance roadway safety and operational efficiency. Candidates should have 1-3 years of Python experience, ML deployment skills, and familiarity with cloud platforms. This position offers an opportunity to work on innovative AI projects within a supportive environment.

Qualifications

• 1–3+ years of production experience with Python.
• Experience deploying 1–3+ ML models.
• Hands-on experience with AWS, Azure, GCP, or OCI.

Responsibilities

• Gather and refine AI solution requirements.
• Develop and transition AI/ML proof-of-concepts.
• Train and validate machine learning models.

Skills

Python

Machine Learning

AWS

Docker

SQL

Bash scripting

Kubernetes

Tools

Azure DevOps

TensorFlow

OpenCV

Job description

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.

Key Responsibilities

• 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.

Stakeholder Engagement & Communication

• 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.

Delivery Excellence & Governance

• 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.

Team Coordination & Support

• 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.

Required Qualifications

• 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 DevOps tools including Docker and Kubernetes.
• Proficiency with SQL and NoSQL/vector databases.
• Strong scripting skills in Bash and PowerShell for automation tasks.
• 1–3+ years of experience working in command-line interfaces for production workflows.

Preferred Qualifications

• Experience with CI/CD tools such as Azure DevOps, GitHub Actions, or Jenkins.
• Production experience in computer vision using frameworks such as PyTorch, TensorFlow, 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.