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About This Role
Welcome to ONE!
While we're headquartered in sunny Arizona, we've always got travel on our minds. We're passionate about creating innovative technology and business solutions that transform travel experience.
The Manager, Contact Center AI \& Insights will partner with the Vice President, Customer Operations, Technology \& Analytics to execute the AI strategy and technology roadmap for contact center innovation. The role will lead the implementation, adoption, and continuous optimization of AI\-powered technologies that enhance agent performance, customer experience, and operational efficiency. Working closely with Contact Center leadership, Technology, Product, and Analytics teams, this role will maximize the value of AI through automation, conversational intelligence, and data\-driven insights.
As the organization's AI subject matter expert for the contact center, this individual will identify opportunities to improve performance, drive AI adoption, and regularly present business outcomes, operational trends, and strategic recommendations to executive leadership.
This opportunity is an onsite role located in Scottsdale, AZ.
Key Responsibilities
AI Platform Strategy \& Optimization
- Partner with the Vice President, Customer Operations, Technology \& Analytics to execute the AI strategy and technology roadmap for contact center innovation, while managing the day\-to\-day administration and continuous optimization of AI\-powered contact center solutions, including agent assistance, automated coaching, conversation intelligence, quality monitoring, workflow automation, and customer communications.
- Configure and optimize AI platforms such as Sense, including prompts, scorecards, coaching models, business rules, and automation workflows to maximize business value.
- Evaluate emerging AI technologies, recommend opportunities and priorities based on operational impact and ROI, and partner with vendors on product enhancements.
Contact Center Performance \& Continuous Improvement
- Partner with Contact Center leadership to improve agent performance, customer experience, and operational efficiency through AI\-driven coaching, conversational intelligence, and workflow automation.
- Identify opportunities to improve quality, compliance, conversion, customer satisfaction, and communication effectiveness across voice, SMS, email, chat, and other customer communication channels.
- Recommend and implement process improvements based on AI insights, operational trends, and customer behavior.
Insights \& Executive Reporting
- Develop executive dashboards and recurring business reviews highlighting AI adoption, contact center performance, communication effectiveness, operational trends, business opportunities, and measurable wins.
- Present actionable insights and strategic recommendations to executive leadership while tracking key performance indicators including QA, CSAT, AHT, FCR, conversion, automation utilization, and ROI.
Program Leadership \& Governance
- Lead AI initiatives from business requirements through implementation, adoption, and continuous optimization while coordinating cross\-functional efforts across Operations, Technology, Product, Analytics, Marketing, and external vendors.
- Establish governance standards for AI configuration, prompt management, quality consistency, and responsible AI usage to ensure scalable, measurable, and reliable business outcomes.
- Bachelor’s degree in business, Information Systems, Computer Science, Operations Management, or a related field (or equivalent experience).
- *Experience in AI product or program management, contact center operations, customer experience, business operations, or related disciplines.*
- *Experience implementing, administering, or optimizing AI\-powered contact center platforms such as Sense, Observe.AI, Cresta, NICE Enlighten, CallMiner, Verint, Gong, or similar technologies.*
- Strong understanding of contact center operations, customer experience, and key performance metrics including QA, CSAT, AHT, FCR, conversion, and operational efficiency.
- Demonstrated experience developing executive dashboards, business reviews, and presenting performance insights and recommendations to senior leadership.
- Proven ability to analyze operational data, identify improvement opportunities, and drive measurable business outcomes through AI and automation.
- Experience leading cross\-functional initiatives involving Operations, Technology, Product, Analytics, and Customer Experience teams.
- Excellent communication, presentation, stakeholder management, organizational, and problem\-solving skills.
- Must successfully complete and clear the company's comprehensive background screening process as a condition of employment.
Preferred Qualifications
- Experience administering Sense or similar AI\-powered conversation intelligence and quality management platforms.
- Experience with Salesforce CRM and contact center technologies such as NICE CXone, Five9, Genesys Cloud, Amazon Connect, or 3CX.
- Experience with Generative AI, prompt engineering, large language models (LLMs), and conversational AI best practices.
- Experience building executive dashboards using Power BI, Tableau, or similar business intelligence platforms.
- Experience leading AI adoption, organizational change management, and digital transformation initiatives.
- Experience supporting customer service, sales, or contact center organizations.
- Experience in the travel, hospitality, membership, or customer experience industries.
What We Offer
- Exclusive Team Member Travel Discounts
- Affordable Medical Insurance
- 100% Employer Paid Dental and Vision Insurance
- HSA with Company Contribution
- 401(k)
- Basic and Voluntary Life \& AD\&D
- Pet Benefits
- Covered Parking
- Amazing Culture!
Office Highlights – Scottsdale, AZ
- Modern office design
- Fully equipped employee gym
- Basketball court to recharge during the day
Physical Requirements
- Prolonged periods of sitting at a desk and working on a computer in a typical office environment.
ONE is an equal opportunity employer. All aspects of employment, including hiring, promotion, discipline, and termination, are based on merit, qualifications, performance, and business needs. We are committed to providing equal employment opportunities without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, disability, veteran status, genetic information, or any other protected status under applicable federal, state, or local law.
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 Open Network Exchange, 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. Mid-level AI roles across all categories have a median of $194,400.
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.
Open Network Exchange AI Hiring
Open Network Exchange has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Scottsdale, AZ, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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