About the role

Head of AI - Platforms at Zayo leading strategic design and deployment of AI solutions. Focused on building scalable AI capabilities and enterprise-grade infrastructure.

Responsibilities

Develop and execute a comprehensive AI platform roadmap aligned with Zayo’s business objectives
Lead the research, evaluation, and implementation of large-scale AI models, including LLMs, transformer-based models, computer vision, and multimodal AI
Design and deploy AI solutions for predictive analytics, intelligent automation, operational optimization, and real-time decision-making
Oversee the end-to-end AI lifecycle, including data engineering, model development, testing, deployment, monitoring, and continuous improvement
Build and optimize scalable AI infrastructure using cloud platforms such as AWS, Azure, or Google Cloud, along with GPU and TPU-based distributed training environments
Implement modern MLOps frameworks to support efficient model deployment, governance, observability, reliability, and performance monitoring
Build, mentor, and lead a high-performing team of AI researchers, machine learning engineers, data engineers, and AI platform specialists
Partner with engineering, network operations, product, data, security, compliance, and business teams to identify high-value AI use cases and ensure business alignment
Establish responsible AI practices that address bias, fairness, transparency, model risk, security, and regulatory compliance
Drive measurable business outcomes through AI-enabled improvements in efficiency, service reliability, customer experience, and operational decision-making.

10+ years of experience in AI, machine learning, data science, or advanced analytics
5+ years of experience leading AI, machine learning, data science, or engineering teams
Expertise in transformer-based models such as GPT, BERT, T5, LLMs, computer vision, and multimodal AI
Hands-on experience with AI frameworks such as TensorFlow, PyTorch, and JAX
Experience designing and deploying large-scale AI systems in production environments
Strong knowledge of MLOps, model monitoring, CI/CD for ML, data pipelines, feature stores, and cloud-based AI infrastructure
Experience with AWS, Azure, or Google Cloud, as well as distributed training using GPUs or TPUs
Demonstrated ability to translate business needs into AI strategies, technical roadmaps, and measurable outcomes
Strong leadership, communication, stakeholder management, and cross-functional collaboration skills
Knowledge of responsible AI, AI governance, model risk management, bias mitigation, and regulatory compliance.