Director Agentic AI Solutions

Company Summary Statement

As one of the largest investor-owned utility companies in the United States, PPL Corporation (NYSE: PPL), is committed to creating long-term, sustainable value for our 3.5 million customers, our shareowners and the communities we serve. Our high-performing regulated utilities — PPL Electric Utilities, Louisville Gas and Electric, Kentucky Utilities and Rhode Island Energy — provide an outstanding experience for our customers, consistently ranking among the best utilities in the nation. PPL’s companies are also addressing challenges head-on by investing in new infrastructure and technology that is creating a smarter, more reliable and resilient energy grid. We are committed to doing our part to advance a cleaner energy future and drive innovation that enables us to achieve net-zero carbon emissions by 2050 while maintaining energy reliability and affordability for the customers and communities we serve. PPL is a positive force in the cities and towns where we do business, providing support for programs and organizations that empower the success of future generations by helping to build and maintain strong, diverse communities today.

Overview This is a hybrid position requiring on-site presence three days per week at one of our local offices, located in Allentown, PA (Lehigh Valley), Louisville, KY, or Providence, RI. #PPLIND

PPL’s Data and AI Office is seeking a Director Agentic AI Solutions and to be an integral part of the OnePPL Digital Transformation to define and deliver our Agentic AI strategy, enabling autonomous, goal-driven agents to transform customer engagement, operational efficiency, and enterprise decision-making. This role will lead the design and deployment of multi-agent architectures, leveraging both off-the-shelf agentic AI capabilities from enterprise applications and custom-built solutions. The ideal candidate combines deep technical expertise with a strong product management mindset and a productionalization mentality, ensuring solutions are scalable, reliable, and supported for long-term success. The role will report to the Sr. Director, Head of Customer AI.

Responsibilities Strategic Leadership

- Develop and own the enterprise Agentic AI roadmap, aligning innovation with business priorities and digital transformation goals.

- Identify high-value use cases for multi-agent systems in customer service, grid operations, predictive maintenance, and energy optimization.

- Partner with executive leadership to translate emerging AI capabilities into measurable business outcomes.

Architecture & Execution

- Architect and operationalize multi-agent frameworks leveraging LLMs, orchestration platforms, and autonomous reasoning engines.

- Integrate off-the-shelf agentic AI capabilities from enterprise platforms (e.g., CRM, ERP, field service) with custom-built agents.

- Drive end-to-end lifecycle: ideation → prototyping → productionalization → ongoing support.

Product Management & Delivery

- Apply a product management mindset to prioritize features, manage roadmaps, and ensure alignment with user needs and business goals.

- Define success metrics, adoption targets, and ROI for agentic AI initiatives.

- Collaborate with business units to ensure solutions deliver tangible value and user satisfaction.

Operationalization & Support

- Establish robust support models for production systems, including monitoring, incident response, and continuous improvement.

- Implement scalable deployment practices leveraging MLOps and cloud-native architectures.

- Ensure reliability, security, and compliance across all agentic AI deployments.

Governance & Ethics

- Create governance frameworks for agent behavior, safety, and compliance with regulatory and ethical standards.

- Implement risk evaluation models for agent adoption and operational readiness.

Innovation & Thought Leadership

- Stay ahead of GenAI, agentic frameworks (LangChain, AutoGen, CrewAI), and orchestration trends.

- Represent the organization in industry forums, conferences, and innovation councils.

- Author thought leadership content and evangelize responsible AI adoption.

Qualifications

Basic Qualifications

:

- Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field; PhD preferred.

- 10+ years of experience in AI/ML, with at least 3 years in leadership roles focused on autonomous systems or multi-agent architectures.

- Proven experience deploying agentic or orchestrated AI systems in production environments.

- Deep understanding of LLMs, reinforcement learning, agent frameworks (e.g., LangChain, AutoGen, CrewAI), and orchestration tools.

- Deep understanding of statistical modeling, machine learning, data mining, and big data technologies (e.g., Hadoop, Spark) Proficiency in programming languages such as Python, R

- Strong grasp of enterprise architecture, cloud platforms (Azure, AWS, GCP), and API ecosystems.

- Excellent communication, stakeholder management, and strategic thinking skills.

- Solid understanding and experience in deploying on GenAI, Data Science and Machine Learning models

- Experience with cloud-based AI and analytics platforms (e.g., AWS, Azure, Databricks, Snowflake etc)

- Ability to resolve complex issues independently and escalate to management as needed.

- Experience establishing metrics/key performance indicators to measure business impact

- Regularly share the business value and impact of AI initiatives with senior leadership through Monthly Business Reviews (MBRs) and other forums.

- Ability to align IT transformation initiatives with the overall business strategy.

- Initiative and passion for finding solutions to ambiguous problems and ability to work in difficult situations and across organizational boundaries.

Preferred Qualifications:

- Utilities industry experience, especially within Electric and Gas Transmission and Distribution

- Familiarity with customer service AI, predictive maintenance, and intelligent automation.

- Knowledge of GenAI safety, interpretability, and alignment techniques.

- Experience with MLOps, vector databases, and retrieval-augmented generation (RAG).