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About This Role
Location:
4910 Tiedeman Road, Brooklyn OhioDepartment: Contact Center Technology
Experience: 4\+ years
### About the Role
KeyBank is seeking a Lead AI Engineer to design, build, and modernize AI\-powered conversational experiences, IVR platforms, and Voice Bot solutions within the Contact Center Technology organization. This role serves as a senior hands\-on engineer responsible for architecting, developing, and delivering AI\-driven solutions that enhance customer self\-service and agent experiences.
Unlike a traditional technical lead role, this position is deeply focused on hands\-on AI engineering and solution delivery. The Lead AI Engineer will spend the majority of their time designing, building, coding, integrating, and optimizing AI\-powered applications while providing technical guidance across initiatives.
The Lead AI Engineer partners closely with product, platform, voice, and operations teams to deliver secure, scalable, and highly reliable conversational AI solutions on Google Cloud Platform (GCP). This is a senior individual contributor role with delivery accountability, but no people management responsibility.
### Key Responsibilities
- Design and develop AI\-powered IVR and voice bot solutions leveraging modern conversational AI frameworks.
- Lead the technical architecture, engineering design, and implementation of AI\-driven customer experience initiatives.
- Build and develop Node.js / TypeScript microservices aligned to cloud\-native and low\-latency voice requirements.
- Own end\-to\-end delivery of complex AI features, from concept and design through deployment and production support.
- Apply agentic AI patterns utilizing Gemini, LangGraph, LangChain, and emerging AI frameworks to enhance conversational experiences.
- Develop, test, evaluate, and optimize prompts, workflows, and AI orchestration strategies.
- Collaborate across the contact center technology ecosystem to support call routing, self\-service, and agent handoff scenarios.
- Provide technical mentorship and code review support while contributing directly to development efforts.
- Establish engineering best practices for AI solution development, testing, deployment, and observability.
- Apply SRE principles to ensure resiliency, scalability, monitoring, and production readiness.
- Ensure solutions comply with security and regulatory requirements (PII / PCI).
### Required Skills \& Qualifications
- 4\+ years of software engineering experience, including 2\+ years as a Lead AI Engineer, Lead Engineer, Principal Engineer, or comparable senior technical contributor role.
- Strong hands\-on experience building and deploying AI\-powered applications and services.
- Strong, hands\-on experience with Node.js / TypeScript.
- Proven experience designing and delivering cloud\-native solutions on GCP.
- Experience implementing and integrating LLMs, conversational AI platforms, and agentic AI frameworks.
- Solid understanding of microservices architecture, APIs, and distributed systems.
- Hands\-on Kubernetes experience, including GKE cluster setup and platform operations.
- Experience with IVR, voice bots, conversational AI, or contact center technologies.
- Strong collaboration skills across engineering, product, and operations teams.
- Excellent technical judgment, communication, and problem\-solving skills.
### Preferred Qualifications
- Experience with conversational AI and agentic frameworks (Gemini, LangGraph, LangChain).
- Experience developing AI agents, orchestration workflows, and multi\-agent solutions.
- Knowledge of voice flows, call routing, containment, and agent escalation.
- Experience working in regulated or financial services environments.
- Familiarity with CI/CD pipelines, containerization, and Infrastructure as Code.
- Experience working in Agile delivery models at enterprise scale.
- Demonstrated experience with Agentic "Vibe Coding" — rapid prototyping and iterative development using AI\-assisted coding tools, prompts, and agent\-driven workflows.
*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 $96,000\.00 \- $181,000\.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: 09/11/2026 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 HR\[email protected].
\#LI\-Remote
Salary Context
This $96K-$181K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($138K) sits 36% below the category median. Disclosed range: $96K to $181K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
KeyBank AI Hiring
KeyBank has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Brooklyn, OH, US, New York, NY, US. Compensation range: $181K - $181K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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