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About This Role
Job Posting End Date
10-19-2026 Please note the job posting will close on the day before the posting end date.
Job Summary
At American Electric Power, the AI Solutions & Enablement team develops the platforms, guardrails, and solutions that help teams across the company work more effectively through artificial intelligence and enterprise automation. We are looking for an experienced Solution Architect to support solution architecture across that portfolio by contributing to reference architectures, evaluating the right capability for each business problem, and applying enterprise standards through practical engineering guardrails. This role will provide architecture support and technical guidance across internal teams and vendor partners, while remaining hands-on enough to validate patterns, help remove delivery blockers, and strengthen solutions in production when needed.Job Description
What You'll Do
Cross-Capability Solution Architecture
- Design solutions using the optimal mix of architectures across AI, automation, integration, and data.
- Contribute to and maintain reference architectures, reusable components, and standard patterns spanning AI/ML and process automation solutions.
- Work with business and technical teams to translate defined needs into scalable, secure designs that internal teams and vendor partners can implement consistently.
Technology Evaluation and Selection Support
- Evaluate AI and automation platforms and services using established enterprise criteria, identify overlap and gaps, and provide recommendations on capabilities to standardize, retire, or pilot.
- Apply and help refine criteria for when each tool is the appropriate choice, including tradeoffs, constraints, and fit-for-purpose considerations.
- Translate those criteria into practical decision guidance, such as selection frameworks, comparison tools, and worked examples, that business partners and delivery teams can use when evaluating solution options.
Platforms, Tooling, and Enablement
- Support the development and improvement of the internal AI and automation platforms and development environments teams use to build solutions, including cloud AI services and process automation platforms.
- Create templates, starter kits, and reusable repositories that reduce friction from build to deploy for engineers, data scientists, and citizen builders.
Responsible AI, Automation Governance, and Risk Partnership
- Apply responsible AI and automation standards through architecture reviews, established checklists, and controls.
- Support solution designs that incorporate security, governance, reliability, and operability, including strong identity and access controls, auditability, observability, maintainability, and attention to the risks unattended automations can introduce.
Vendor Delivery Support
- Review vendor architectures, code, and technical deliveries for fit, security, operability, and alignment to internal standards and patterns.
- Provide technical guidance, help resolve delivery issues, and support alignment among stakeholders.
Preferred & Essential Skills
- Experience contributing to the design of enterprise solutions across more than one capability, including AI/ML, process automation, integration, or data, with the ability to evaluate options deliberately.
- Experience with enterprise AI, automation, integration, and cloud platforms, including Azure, AWS, Microsoft Power Platform, Copilot, and related API and integration services, with the ability to evaluate when each technology is the appropriate fit.
- Experience applying or contributing to tool selection criteria, reference patterns, and solution blueprints used by other teams.
- Strong AI/ML workflow and practical MLOps knowledge, plus working experience with enterprise automation and integration patterns; hands-on when needed.
- Security and governance fundamentals applied to AI and automated systems, with experience reviewing vendor technical deliverables and spotting risks early.
- Effective communication and collaboration across business, technical, and vendor teams; balances standards with delivery realities and stays current with emerging practices.
What We're Looking For:
Education requirements are listed below:
- Bachelor's degree in computer science, engineering, or related technical field is required.
Work Experience requirement listed below:
- 6 years of relevant work experience is required.An equivalent combination of education and related experience may be considered.
What You'll Get:
- Base salary
- Annual bonus
- Long-term incentive
- 401(k) match
- AEP Pension
- Comprehensive benefits package designed to support and enhance the overall well-being of employees.
At AEP, we’re more than just an energy company — we’re a team of dedicated professionals committed to delivering safe, reliable, and innovative energy solutions. Guided by our mission to put the customer first, we strive to exceed expectations by listening, responding, and continuously improving the way we serve our communities. If you're passionate about making a meaningful impact and being part of a forward-thinking organization, this is the company for you!
Compensation Data
Compensation Grade:
SP20-008Compensation Range:
$98,993.00 - $128,688.00 The Physical Demand Level for this job is: S – Sedentary Work: Exerting up to 10 pounds of force occasionally (Occasionally: activity or condition exists up to 1/3 of the time) and/or a negligible amount of force frequently. (Frequently: activity or condition exists from 1/3 to 2/3 of the time) to lift, carry, push, pull or otherwise move objects, including the human body. Sedentary work involves sitting most of the time but may involve walking or standing for brief periods of time. Jobs are sedentary if walking and standing are required only occasionally, and all other sedentary criteria are met.
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It is hereby reaffirmed that it is the policy of American Electric Power (AEP) to provide Equal Employment Opportunity in all respects of the employer-employee relationship including recruiting, hiring, upgrading and promotion, conditions and privileges of employment, company sponsored training programs, educational assistance, social and recreational programs, compensation, benefits, transfers, discipline, layoffs and termination of employment to all employees and applicants without discrimination because of race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, veteran or military status, disability, genetic information, or any other basis prohibited by applicable law. When required by law, we might record certain information or applicants for employment may be invited to voluntarily disclose protected characteristics.
Salary Context
This $98K-$128K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At American Electric Power, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($113K) sits 46% below the category median. Disclosed range: $98K to $128K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
American Electric Power AI Hiring
American Electric Power has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Columbus, OH, US. Compensation range: $128K - $177K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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