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60 High Tech Artificial Intelligence Engineer Jobs Hiring Near You

Hireblazer

Artificial Intelligence Engineer (AI/ML)

Austin, TX · On-site

$113K - $136K/yr

Artificial Intelligence Engineer (AI/ML) Duration: 12 Months Location: Austin, TX 78744 Note: This ... The role will be embedded within the Traffic Technology team with the goal to develop AI/ML for ...

SQDM

USA_Artificial Intelligence Engineer

Frisco, TX · On-site +1

$114K - $151K/yr

... estate tech space and are looking for a Senior AI Engineer to join our founding AI team. You'll ... high-performing systems. Key Responsibilities AI Agent Development * Design, build, and maintain ...

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Artificial Intelligence Engineer (AI/ML)

Hireblazer

Austin, TX • On-site

$113K - $136K/yr

Contractor

Re-posted 25 days ago

Job description

Role: Artificial Intelligence Engineer (AI/ML)

Duration: 12 Months

Location: Austin, TX 78744

Note: This role requires onsite presence 4–5 days per week.

The client work to be accomplished:

The Artificial Intelligence (AI) engineer will develop AI/ML proof-of-concept demonstrations and build new AI/ML solutions that scale with the client pipelines and workflows. The role will be embedded within the Traffic Technology team with the goal to develop AI/ML for Operational Technology that improves the safety and operations of the client roadway system. The AI engineer will work across teams such as Traffic Technology, ITD AI team, and TRF to gather business requirements, develop AI software, and demonstrate successful solutions to end-users.

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 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.

Delivery Excellence & Governance

Ensure automation projects comply with the client’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.

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.

Required Skills:

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, GCP, or OCI for deploying and managing ML workloads. We leverage AI/ML tools across all major cloud providers (Azure AI, AWS SageMaker/Bedrock, GCP 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)