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About This Role
Job Posting End Date
07\-19\-2026
Please note the job posting will close on the day before the posting end date.
Job Summary
Responsible for large\-scale security assignments providing direction to other team members. Responsible for gathering, investigating, and analyzing very complex security requirements, processes, and incidents. Leads analysis of security controls assessments (internal and third party) through application security testing, penetration testing or other means to ensure controls effectiveness. Leads the identification and documentation of potential mitigations /remediations and ensures report creation of findings with identified risk response. Responsible for the conceptual design of implementation strategies on assigned security projects/activities. Leads advanced level implementation, support, and/or usage of technical solutions. Leads others in advanced problem solving, decision\-making, and functional area knowledge. Mentors and provides functional/technical work direction to team.Job Description
Responsible for integrating, optimizing, and operationalizing artificial intelligence (AI) capabilities within the security operations center. This role bridges cybersecurity operations and advanced analytics, leveraging AI\-driven tools to enhance threat detection, automate response workflows, and improve overall security posture. The analyst will work closely with SOC analysts, detection engineering, and data teams to ensure AI solutions are effectively deployed, tuned and aligned with real\-world security use cases.
What You'll Do:
Essential Job Functions \& Tasks
- Design and enhance fusion center capabilities by integrating cross\-domain intelligence, improving visibility, and enabling enterprise\-wide situational awareness and decision making.
- Develop and deploy AI models to support alert triage, prioritization and anomaly detection.
- Design, implement and maintain AI driven detection and response capabilities within the SOC.
- Analyze large scale security telemetry to identify patterns, threats and opportunities for detection improvement.
- Collaborate with SOC analysts and engineers to identify and implement AI\-assisted decision making.
- Integrate AI\-driven enrichment and correlation into existing security tooling.
- Automate repetitive SOC tasks using AI and scripting.
- Create dashboard and metrics to measure SOC performance improvements.
- Support development of AI assisted investigation workflows and SOC copilots.
- Apply a creative, analytical mindset to SOC operations, intelligence, and physical/cyber security data, developing new approaches to identify trends, risks, and actionable insights.
- Monitor model performance, reduce false positives/negatives, and continuously improve detection quality.
- Partner with data engineers to ensure proper data ingestion, normalization, and feature engineering.
- Document AI use cases, workflows and operational playbooks.
- Stay current on emerging threats, adversary tactics and advancements in AI for cybersecurity.
Nice\-to\-have
- Advanced experience with machine learning, data science or AI applications in security.
- Ability to analyze threat patterns and assess enterprise impact.
- Hands on experience with SIEM platforms.
- Experience with scripting or programming.
- Familiarity with MITRE ATT\&CK, MITRE ATLAS and common adversary tactics and techniques.
- Exposure to SOAR platforms and security automation workflows.
- Experience working with large datasets, log analysis, or telemetry pipelines.
- Strong interpersonal, investigative, and critical thinking skills.
- Adaptable to changing priorities and high\-pressure environments.
- Active or eligible for SECRET DoD clearance.
Certifications:
- SANS GCIA, GCIH, GAIPS, GASAE, GOAA
- CAISP
- Google Professional Machine Learning Engineer
- AWS Certified Machine Learning
- IBM Certified AI Engineer
What We're Looking For:
Security Spec Sr (SG7\)
Education requirements are listed below:
- Bachelor's degree OR Associates degree with 2 years relevant experience in system administration/help desk/security (cyber or physical) OR High School Diploma/GED with 3 years relevant experience in IT system administration/help desk/security (cyber or physical); OR graduation from an approved Cybersecurity Program; alternatively may have non\-degree qualifications (such as hands\-on demonstrated ability in a technical interview/assessment).
Work Experience requirement listed below:
- 2 or more years of Information Technology related experience; OR 1 or more years of security related experience, which may include military/government work experience in addition to any experience identified above.
Security Spec Prin (SG8\)
Education requirements are listed below:
- Bachelor's degree OR Associates degree with 2 years relevant experience in system administration/help desk/security (cyber or physical) OR High School Diploma/GED with 4 years relevant experience in IT system administration/help desk/security (cyber or physical); OR graduation from an approved Cybersecurity Program; alternatively may have non\-degree qualifications (such as hands\-on demonstrated ability in a technical interview/assessment).
Work Experience requirement listed below:
- 4 or more years of Information Technology related experience; OR 2 or more years of security related experience, which may include military/government work experience in addition to any experience identified above.
Security Spec Lead (SG9\)
Education requirements are listed below:
- Bachelor's degree OR Associates degree with 2 years relevant experience in system administration/help desk/security (cyber or physical) OR High School Diploma/GED with 4 years relevant experience in IT system administration/help desk/security (cyber or physical).
Work Experience requirement listed below:
- 7 or more years of Information Technology related experience; OR 5 or more years of security related experience, which may include military/government work experience in addition to any experience identified above.
Security Spec Staff (SG10\)
Education requirements are listed below:
- Bachelor's degree OR Associates degree with 2 years relevant experience in system administration/help desk/security (cyber or physical) OR High School Diploma/GED with 4 years relevant experience in IT system administration/help desk/security (cyber or physical).
Work Experience requirement listed below:
- 10 or more years of Information Technology related experience OR 8 or more years of security related experience, which may include military/government work experience in addition to any experience identified above.
What You'll Get:
Base Salary from $87,633\.00 \- $177,503\.00 /year. In addition to a competitive compensation, AEP offers a unique comprehensive benefits package that aims to support and enhance the overall well\-being of our 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\-009Compensation Range:
$87,633\.00 \- $177,503\.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 $87K-$177K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($132K) sits 39% below the category median. Disclosed range: $87K to $177K.
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.
American Electric Power AI Hiring
American Electric Power has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Columbus, OH, US. Compensation range: $177K - $177K.
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/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 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).
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 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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