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# Head of AI - Platform AI

- **Company:** ZAYO
- **Location:** Jefferson City, MO, United States (Remote available)
- **Experience:** Expert
- **Salary:** $215,000.0 - $245,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Computer Vision, Microsoft Azure, Cloud Computing, Information Engineering, Tensorflow, Software Deployment, AI Infrastructure, Graphics Processing Unit (GPU), Google Cloud, Pytorch, Large Language Models, Deep Learning, Machine Learning Operations, Multiaccess Edge Computing, GPT
- **Published:** October 22, 2026
- **Apply:** https://dejobs.org/x/x/F688C4927FC140EA9EAFA53114747085/job/

## About the Role

* Minimum of ten (10) years of experience in AI research and deployment, with at least 5 years in leadership roles.
 * Deep expertise in transformer-based models (e.g., GPT, BERT, T5), computer vision, and multimodal AI.
 * Hands-on experience with AI frameworks such as TensorFlow, PyTorch, and JAX.
 * Proven track record of deploying large-scale AI systems with measurable business impact.

## Description

Our Head of AI will lead the strategic design, development, and deployment of AI-driven solutions across the organization. This role focuses on implementing cutting-edge machine learning, deep learning, and automation technologies to drive innovation, enhance operational efficiency, and deliver measurable business outcomes. Reporting to the VP, Architecture, Cloud & AI, this leader will advance the organization's AI capabilities and foster a culture of continuous innovation., AI Strategy and Innovation:

 * Develop and execute a comprehensive AI roadmap aligned with business objectives.
 * Lead the research and implementation of large-scale AI models (e.g., LLMs, Vision Transformers) and automation frameworks.

AI System Development:

 * Design and deploy AI solutions for predictive analytics, intelligent automation, and real-time decision systems.
 * Lead the end-to-end lifecycle of AI projects, including data engineering, model development, testing, and production deployment.

AI Infrastructure and Scalability:

 * Build and optimize infrastructure for distributed AI training using GPUs, TPUs, and cloud platforms such as AWS, Azure, and Google Cloud.
 * Implement advanced MLOps frameworks for efficient model deployment and monitoring.

Team Leadership:

 * Build and lead a high-performing team of AI researchers and engineers.
 * Foster collaboration between AI teams and cross-functional departments to ensure alignment with business goals.

Ethics and Compliance:

 * Ensure the development of responsible AI systems that address bias, fairness, and regulatory compliance.