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POSITION SUMMARY
The Knowledge Manager serves as the primary owner of Customer Care knowledge management strategy, responsible for the creation, governance, maintenance, and continuous improvement of operational documentation, knowledge assets, and AI-enabled knowledge systems. Working cross-functionally with Operations, Compliance, Product, Technology, Training, Quality Assurance, and Data & Analytics, this role ensures information is accurate, accessible, compliant, scalable, and AI-ready.
This position plays a critical role in supporting CarePayment's AI strategy. As the organization's primary technical writer and knowledge management expert, the Knowledge Manager develops and governs operational documentation and knowledge assets while partnering with Training & Development to ensure employee and AI training materials remain aligned. The role ensures employees and AI systems have access to accurate, current, and compliant information that supports scalable growth.
ESSENTIAL JOB DUTIES
Knowledge Management & Documentation
- Own the Customer Care knowledge management program, including governance, content standards, taxonomy, organization, and maintenance.
- Create, revise, and maintain operational procedures, work instructions, quick-reference guides, knowledge articles, and process documentation.
- Establish and maintain content ownership, review cycles, version control, and approval workflows.
- Audit existing documentation for accuracy, consistency, usability, and compliance.
- Partner with business stakeholders to identify documentation gaps and process improvement opportunities.
- Ensure knowledge assets are organized, maintained, and optimized for both employee accessibility and AI consumption, supporting operational efficiency and scalable automation.
- AI Enablement & Governance
- Lead the development and maintenance of AI-ready knowledge repositories and approved content libraries.
- Coordinate ingestion of approved templates, talk-tracks, business rules, process documentation, and knowledge assets into AI-enabled systems.
- Assist with AI testing, validation, prompt optimization, and reviews of content quality, transparency, and accuracy.
- Maintain governance controls to ensure AI-generated content aligns with approved language, compliance requirements, bank approval processes, and brand standards.
- Monitor AI feedback loops, identify content improvement opportunities, and support ongoing AI training efforts.
- Participate in AI governance activities, documentation reviews, and implementation planning.
Cross-Functional Collaboration
- Partner with Compliance to ensure documentation and knowledge assets align with regulatory requirements and internal policies.
- Collaborate with Technology, Product, and Data & Analytics teams on knowledge architecture, integrations, and AI initiatives.
- Support Training, Quality Assurance, and Operations teams by ensuring learning materials remain current and aligned.
- Participate in operational projects requiring procedure development, workflow design, communication plans, and change management support.
Continuous Improvement
- Analyze knowledge usage, search effectiveness, content quality, and user feedback to identify improvement opportunities.
- Recommend process improvements that enhance documentation quality, employee experience, customer experience, and operational efficiency.
- Establish metrics and reporting to measure knowledge management effectiveness, AI readiness, content quality, system transparency/performance, and auditability.
And of course, we’re growing, so this list may adapt and change over time
REQUIRED QUALIFICATIONS, KNOWLEDGE, SKILLS AND ABILITITES
- Bachelor’s degree in Business, Communications, Technical Writing, Information Management, Instructional Design, or related field; equivalent experience considered.
- 3+ years of experience in knowledge management, technical writing, business process documentation, training development, operations, or related disciplines.
- Strong writing, editing, and documentation skills with exceptional attention to detail.
- Experience developing and maintaining policies, procedures, work instructions, or operational documentation.
- Demonstrated ability to simplify complex information into clear, user-friendly content.
- Strong organizational, project management, and stakeholder management skills.
- Proficiency with Microsoft 365 applications, including Word, Excel, SharePoint, Teams, Visio and Copilot.
- Personal skills and abilities:
- Ability to translate technical requirements into dependable, maintainable data solutions.
- Ability to communicate clearly with technical and nontechnical stakeholders.
- Strong analytical, troubleshooting, documentation, and time-management skills.
- Strong initiative, ownership, attention to detail, and adaptability in a growing environment.
- Ability to work independently and collaboratively across departments while managing competing priorities.
- Dedication to exceptional, professional work through solution orientation, confidentiality and attention to detail, follow-up and effective communication.
- Excellent communication (verbal and written in English; feedback, listening and comprehension), documentation, detail and time management skills (prioritizing and multitasking). Ability to communicate information and ideas to others so they will understand.
- Ability to build rapport and work productively and professionally with others (customers and teammates); ability to work independently.
- Strong sense of initiative and ownership over work and ability to adapt focus and work as necessary. Ability to work effectively and complete assigned tasks with minimal supervision. Flexibility with changing procedures and goals.
- Ability to be receptive to coaching and feedback and to apply to work.
PREFERRED QUALIFICATIONS
- Experience supporting contact center, healthcare, financial services, fintech, or regulated environments.
- Experience with knowledge management platforms and content governance programs.
- Experience with AI-enabled technologies, chatbots, virtual assistants, large language models (LLMs), or AI governance frameworks.
- Familiarity with compliance requirements related to HIPAA, GLBA, privacy, security, and regulated customer communications.
- Experience with process mapping, workflow design, and change management.
KEY COMPETENCIES
- Technical Writing
- Knowledge Management
- AI Governance & Content Management
- Business Process Documentation
- Content Organization & Information Architecture
- Cross-Functional Collaboration
- Continuous Improvement
- Project Management
- Change Management
- Regulatory & Compliance Awareness
- Analytical Thinking
- Communication & Stakeholder Management
WHAT IS THE WORK ENVIRONMENT LIKE?
- Normal office working conditions. Work requires constant sitting/standing at a desk, working with a computer. This position requires standing, walking, sitting, using hands, seeing, reaching, talking, writing, and hearing; it may require occasionally carrying or lifting equipment (up to 15 pounds).
- Position can be remote. You may need to occasionally visit or work in the office or travel to remote meetings. Remote work setup must mimic in office conditions.
- Position is exempt, salaried. Work hours full-time and may require hours that exceed normal working hours per day during peak periods.
- Position requires contact with others - in meetings, by phone or by email. Interactions focus on data collection, problem solving, service and documentation. Interactions are initiated in person or electronically.
WHY SHOULD YOU WORK HERE?
- Group benefits plans, including medical, dental, vision, FSA/HSA, including retirement plans (401k). Company-paid short-term and long-term disability plans and life insurance. Company benefits begin on the first of the month following the hire date.
- Flexible vacation and nine holidays.
- This position qualifies for annual bonus.
- We believe feedback makes us better. You can expect regular meetings with your manager about your performance and growth.
Pay: $85,000.00 - $131,000.00 per year
Benefits:
- 401(k)
- 401(k) matching
- AD&D insurance
- Dental insurance
- Disability insurance
- Employee assistance program
- Flexible schedule
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Vision insurance
Experience:
- Technical writing: 3 years (Preferred)
- AI content management: 1 year (Preferred)
- Knowledge management: 3 years (Preferred)
Work Location: Remote
Salary Context
This $85K-$131K 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 CarePayment Technologies, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($108K) sits 49% below the category median. Disclosed range: $85K to $131K.
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.
CarePayment Technologies AI Hiring
CarePayment Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $131K - $131K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% of all AI roles offer remote work.
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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