Director / Senior Director – Vertical AI Product Lead (Energy & Utilities)

Job Description

This is a remote position.

Location: United States (remote, with travel to customer sites, workshops and industry events)

Employment type: Full-time, permanent

Compensation: Highly competitive base salary, performance-based variable pay and benefits

Confidential search. HYREON Talent Solutions is conducting this search on behalf of a confidential client. The client’s identity will be disclosed to qualified candidates during the initial conversation.

About our client

Our client is one of the fastest-growing AI and digital transformation firms in North America’s energy and utility sector. It is built exclusively for large electric, gas, water and telecom utilities, helping them modernize the grid and put AI to work at scale.

The company integrates the enterprise platforms utilities run on, including asset management, ERP and cloud systems, and deploys grid modernization frameworks. It is an active member of a leading industry alliance for utility energy-data standards.

Its platforms are already live with North American utilities. Recent deployments include an eMobility platform that manages EV onboarding, charging networks and energy marketplaces, and a workforce automation platform that shortens storm and outage restoration times.

With North American operations expanding quickly, the company is investing in senior US leadership to scale its AI practice.

The Opportunity

Our client is seeking a hands-on, client-facing AI leader to help utilities move from experimentation to enterprise-scale AI. This role sits at the intersection of technology, domain expertise and client advisory, shaping how utilities design, govern and scale AI across their operations.

In this role, you will be a trusted AI advisor to utility customers, combining deep technical expertise with a strong understanding of the utility business. You will build and demo proofs of concept, lead workshops and presentations, shape proposals, and help clients move from experimentation to AI in production. You will work closely with sales leaders, executives and domain experts across the utility value chain, and help build partnerships across the AI ecosystem.

This is a builder’s role in a consulting model: you will design, sell and deliver. As the practice grows, so will the team around you.

What you’ll do

• Lead the development and deployment of AI/ML and Vertical AI solutions for Energy and Utilities.
• Support the AI product vision, prioritize use cases and execute against roadmap priorities aligned to business value.
• Translate insights into product requirements, user stories, success metrics and launch plans.
• Serve as a business and technical leader for model architecture, feature engineering, training pipelines and inference services.
• Oversee model validation, explainability, bias testing and audit readiness.
• Collaborate with product and business leaders to translate use cases into scalable AI features.
• Build proofs of concept, demo them to customers and iterate toward production solutions.
• Advise utilities on scaling AI across the enterprise, including infrastructure, security and governance.
• Engage customers through presentations, workshops, conferences and proposals.
• Build partnerships with leading AI platform and cloud providers.
• Build deep, trusted partner relationships with clients and partners, co-innovating on Energy and Utilities AI use cases and those from similar industries.
• Work with clients and partners on operations and business strategy: measuring innovation and satisfaction, understanding challenges and offering help when needed.
• Provide thought leadership and innovation, with the confidence to challenge business strategy with the client.

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Core AI concepts and technologies:

• Machine learning and modeling: supervised, unsupervised and reinforcement learning; deep learning (CNNs, RNNs, Transformers); NLP and LLMs; generative AI (diffusion models, fine-tuning, RAG).
• AI engineering and MLOps: model training, deployment, monitoring and retraining; feature stores, vector databases and model registries; CI/CD pipelines for ML; GPU and accelerator compute architectures.
• Cloud and infrastructure: Azure AI and Azure ML, AWS SageMaker or Google Vertex AI; Kubernetes, containerization and microservices; data platforms such as Databricks, Snowflake and Synapse.
• Responsible AI and governance: model explainability (SHAP, LIME); fairness, bias detection and…