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About This Role
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:
NOTE: This is a hybrid role requiring 3 days a week on\-site at one of our local offices. OFFICE LOCATIONS: Allentown, PA; Louisville, KY or Melrose, RI \#LI\-Hybrid \#INDPPL
PPL is seeking an experienced Agentic AI Architect to lead the design and delivery of enterprise\-scale AI platform capabilities and next\-generation AI solutions leveraging Generative AI and multi\-agent systems. This role will drive AI\-enabled automation, decision intelligence, and operational transformation across critical enterprise value streams including Meter\-to\-Cash, outage management, advanced metering infrastructure (AMI), geospatial systems, customer experience, safety, and regulatory functions.
Operating within the Data \& AI organization, this role requires strong cross\-functional leadership and close partnership across Enterprise Architecture, Infrastructure, Infrastructure Operations, and the AI Center of Excellence (CoE) to ensure AI solutions are scalable, secure, reliable, and compliant with regulatory standards. The Architect will play a key role in defining and executing the enterprise AI platform strategy, contributing to data and AI governance functions, and enabling reusable patterns, shared services, and governed AI capabilities across the organization.
This position combines deep technical expertise in AI/ML and platform architecture with business acumen and leadership, translating complex business needs into production\-grade AI solutions that deliver measurable value. The role also operates within value stream\-based delivery models, actively contributes to enterprise governance through architecture review boards and standards definition, and drives innovation and transformation in AI capabilities across the enterprise.
Responsibilities:
- Lead design and delivery of AI solutions across enterprise value streams such as ZeroOps, Meter\-to\-Cash, Outage Management, Advanced Metering Infrastructure (AMI), Geospatial systems, Safety, Customer 360, and Asset 360
- Define scalable architecture patterns including agent orchestration, reasoning loops, workflow automation, and human\-in\-the\-loop controls
- Own AI platform and infrastructure architecture, including integration with cloud (Azure), data platforms (Databricks, ADLS, Snowflake), and enterprise systems (SAP, CIS, CRM, OMS, GIS)
- Drive implementation of AI platform capabilities including model lifecycle management, orchestration, observability, monitoring, and performance optimization
- Partner closely with Enterprise Architecture, Infrastructure, Infrastructure Operations, and AI CoE to align on standards, patterns, and operational readiness
- Provide cross\-functional leadership across Data \& AI, EA, Infrastructure, AI CoE, and business teams to ensure end\-to\-end solution delivery
- Contribute to data and AI governance functions, including standards, policies, and responsible AI practices
- Drive innovation and transformation initiatives in AI adoption, platform capabilities, and enterprise\-scale deployment
- Define reusable AI patterns, frameworks, and shared services to accelerate enterprise adoption.
- Operate within value stream\-oriented ways of working, aligning architecture and delivery to business outcomes
- Participate in Enterprise Architecture governance boards, design reviews, and technology standards definition
- Lead cross\-functional collaboration to translate business needs into scalable, production\-grade AI solutions
- Oversee POCs, pilots, and production deployments while mentoring teams and building enterprise AI capability
- Performs other duties as assigned
- Complies with all policies and standards
Qualifications:
Required Education:
- Bachelor's degree.
Required Experience:
- 15\+ years' experience.
- Experience in utilities, energy, or other regulated industries.
- Strong expertise in Generative AI, LLMs, and/or multi\-agent systems.
- Experience designing and delivering enterprise\-scale platforms, including AI/ML or data platforms.
- Hands\-on experience with cloud platforms (Azure preferred), APIs, microservices, and distributed architectures
- Experience with AI platform components such as orchestration, model lifecycle management, monitoring, and observability
- Demonstrated cross\-functional leadership experience across Data \& AI, Enterprise Architecture, Infrastructure, Infrastructure Operations, and AI CoE
- Experience contributing to data and AI governance frameworks, standards, or operating models
- Experience working in value stream\-based delivery models and agile ways of working
- Experience participating in architecture governance forums (e.g., Enterprise Architecture review boards)
- Strong understanding of security, data governance, and regulatory compliance frameworks
- Proven ability to influence senior stakeholders and drive enterprise alignment
- Strong communication, problem\-solving, and consulting skills
Preferred Qualifications:
- Experience delivering AI use cases across domains such as operations, customer, safety, asset management, outage management, AMI, or geospatial systems
- Exposure to SAP IS\-U / S/4 Utilities or similar enterprise platforms
- Experience implementing AI solutions for regulatory use cases (e.g., rate case, audit, compliance automation)
- Familiarity with operational technologies such as OMS, SCADA, GIS, or grid systems.
- Experience building reusable platforms, frameworks, or enterprise AI marketplaces
- Experience leading platform modernization or digital transformation initiatives
- Experience operating in large, matrixed organizations with multiple cross\-functional dependencies
- Experience driving innovation programs or AI transformation initiatives at enterprise scale
- Scope \& Impact
- Lead architecture and delivery of enterprise AI platform and solution capabilities across multiple value streams
- Shape enterprise AI strategy, roadmap, and target\-state architecture
- Drive standardization, governance, and best practices across AI programs
- Enable scalable, reusable AI capabilities across the organization
- Influence platform investment decisions and technology direction
- Partner with senior stakeholders across IT, operations, and regulatory functions
- Act as a key bridge between Data \& AI, Enterprise Architecture, Infrastructure, Infrastructure Operations, and AI CoE
- Represent AI architecture within enterprise governance bodies and architecture review boards
- Contribute to enterprise data and AI governance and policy evolution
- Drive innovation and transformation in AI capabilities, adoption, and operating models
- Mentor teams and build organizational capability in AI, platform engineering, and architecture practices
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,708 AI roles we're tracking, AI Architect positions make up 1% of the market. At PPL Corporation, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $254,798 based on 67 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
PPL Corporation AI Hiring
PPL Corporation has 1 open AI role right now. They're hiring across AI Architect. Based in Allentown, PA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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