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Location:
4910 Tiedeman Road, Brooklyn OhioLocation
Hybrid, 2 days in office - 4910 Tiedeman Rd, Brooklyn, OH
Job Summary
The GenAI Communications & Enablement Lead serves as the primary communications, engagement, knowledge management, and enablement resource for the Enterprise GenAI Governance & Enablement function. This role develops stakeholder-facing content, maintains AI knowledge resources, coordinates engagement programs and forums, and supports awareness, adoption, and readiness efforts for approved AI capabilities and services.
The role also provides communications and enablement support for initiatives across the AI Engineering, Governance, and Architecture organization, with a significant focus on AI Engineering Domain communications, stakeholder engagement, knowledge management, and organizational readiness activities.
The individual partners with Enterprise Communications and communications teams across Lines of Business and Shared Services to develop, coordinate, and amplify content through established channels while supporting the successful adoption of AI capabilities across the organization.
Essential Functions
- Develop communications and engagement materials supporting AI governance, enablement, and AI Engineering initiatives.
- Create stakeholder-facing content including presentations, briefings, newsletters, FAQs, announcements, executive summaries, talking points, communication plans, and adoption materials.
- Maintain GenAI-related SharePoint sites, knowledge repositories, resource hubs, guidance materials, playbooks, FAQs, and content libraries.
- Develop communications plans, launch materials, adoption resources, quick-reference guides, and enablement materials supporting approved GenAI capabilities and services.
- Coordinate the development and publication of user-facing resources supporting AI capabilities, governance processes, and enablement programs.
- Support communications, engagement, awareness, and community-building activities for the AI Engineering Domain, including forums, demonstrations, office hours, and stakeholder events.
- Partner with capability owners, technology teams, subject matter experts, and business stakeholders to develop practical guidance and enablement resources.
- Gather stakeholder feedback and support continuous improvement of communications, engagement, and adoption activities.
- Partner with Enterprise Communications and communications teams across Lines of Business and Shared Services to coordinate and amplify AI-related content through established channels.
- Perform other duties as assigned; duties, responsibilities, and activities may change or new ones may be assigned at any time with or without notice.
- Comply with all KeyBank policies and procedures, including acting professionally at all times, conducting business ethically, avoiding conflicts of interest, and acting in the best interests of Key and its clients.
Education
- Bachelor's Degree or equivalent experience preferred.
Work Experience
- 5+ years of experience in business analysis, communications, stakeholder engagement, change management, knowledge management, enablement, technology program support, or related disciplines.
- Experience supporting technology, software engineering, digital transformation, AI, or enterprise platform initiatives preferred.
- Experience developing communications, enablement resources, adoption programs, training-support materials, or knowledge-management solutions preferred.
Skills
- Ability to develop clear, concise, and effective communications for technical and non-technical audiences.
- Ability to create and maintain knowledge-management resources, content repositories, and stakeholder-facing materials.
- Ability to coordinate communications and engagement activities across multiple stakeholder groups and organizations.
- Ability to develop communications plans, adoption resources, playbooks, FAQs, launch materials, and user guidance.
- Ability to gather, analyze, and incorporate stakeholder feedback to improve communications and user experience.
- Ability to organize and facilitate forums, stakeholder engagement activities, and enablement programs.
- Strong presentation-development, storytelling, and executive-communication skills.
- Experience with SharePoint, collaboration platforms, content management, and communications planning.
- Familiarity with Generative AI technologies, AI governance concepts, and enterprise technology enablement activities.
Core Competencies
- Business Analysis
- Stakeholder Management
- Written and Verbal Communication
- Change Enablement
- Knowledge Management
- Content Development
- Organizational Readiness
- Collaboration and Influence
- Program Coordination
- Presentation Development
Physical Demands
- General office environment requiring prolonged sitting, frequent use of a computer and related technology, and the ability to communicate effectively with teammates and stakeholders in person, virtually, and by telephone.
This position is NOT eligible for employment visa sponsorship for non-U.S. citizens.
COMPENSATION AND BENEFITS
This position is eligible to earn a base salary in the range of $71,000.00 - $125,000.00 annually. Placement within the pay range may differ based upon various factors, including but not limited to skills, experience and geographic location. Compensation for this role also includes eligibility for incentive compensation which may include production, commission, and/or discretionary incentives. Please click here for a list of benefits for which this position is eligible.
Key has implemented an approach to employee workspaces which prioritizes in-office presence, while providing flexible options in circumstances where roles can be performed effectively in a mobile environment.
Job Posting Expiration Date: 12/04/2026 KeyCorp is an Equal Opportunity Employer committed to sustaining an inclusive culture. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, pregnancy, disability, veteran status or any other characteristic protected by law. Qualified individuals with disabilities or disabled veterans who are unable or limited in their ability to apply on this site may request reasonable accommodations by emailing [email protected].
#LI-Hybrid
Salary Context
This $71K-$125K 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 KeyBank, 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($98K) sits 54% below the category median. Disclosed range: $71K to $125K.
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.
KeyBank AI Hiring
KeyBank has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Brooklyn, OH, US. Compensation range: $125K - $125K.
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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