Interested in this AI/ML Engineer role at CSG?
Apply Now →Skills & Technologies
About This Role
Hi, I'm Cassie Alexander, your Recruiter and guide to joining CSG! We are excited to learn more about you and your unique background.
The Senior Manager, Underwriting \- AI Transformation is a strategic, hands\-on builder responsible for reimagining how we underwrite risk using data, automation, and AI. This role is not a traditional underwriting manager position; it sits above day\-to\-day case decisioning and people management, and instead focuses on designing, implementing, and scaling next generation merchant underwriting processes, tools, and strategies that materially improve speed, consistency, and portfolio outcomes.
You will act as a transformation owner: diagnosing current gaps, designing the future state operating model, and building AI driven solutions, workflows, and agents.
We are looking for a Senior Manager, Underwriting – AI Transformation who will:
- Define the multi year vision and roadmap for AI enabled underwriting, aligned with the company’s growth, risk appetite, and operational goals.
- Assess the current underwriting lifecycle (intake, analysis, decisioning, documentation, monitoring) and identify specific opportunities for automation, digitization, and AI assisted workflows.
- Translate strategic objectives (speed, loss performance, scalability, customer experience) into concrete underwriting process changes, tools, and success metrics.
- Design and implement AI and rule based decisioning frameworks that augment underwriters and agents, reduce manual effort, and create explainable, repeatable decisions.
- Architect workflows that use LLMs and other AI tools to automate document ingestion and analysis (e.g., financials, bank statements, contracts, KYB docs, site reviews), and convert them into structured inputs and draft risk assessments.
- Partner closely with data science, engineering, and product teams to specify requirements for models, rules engines, and decisioning platforms, including features, thresholds, and human in the loop review logic.
- Define and maintain the configuration of scorecards, rule sets, and policy rules within underwriting systems to reflect evolving risk appetite and business needs.
- Redesign end to end underwriting processes with automation as a first principle, minimizing manual steps, duplicate work, and unstructured decision making.
- Implement standardized templates, checklists, and decision frameworks that embed data, AI insights, and best practices into day to day underwriting workflows.
- Define and track operational KPIs (e.g., decision turnaround time, straight through processing rate, manual touch rate, rework, error rates) and drive initiatives that move these indicators meaningfully.
- Ensure all new processes and tools meet requirements for control, auditability, explainability, and regulatory/compliance expectations (particularly where AI is involved).
- Serve as the primary transformation lead for underwriting, working with Underwriting Management, Risk, Commercial, Legal, and Operations to align on new ways of working.
- Lead structured discovery sessions, pilots, and rollouts to introduce new AI tools, rules, and processes, ensuring they are usable, resilient, and well adopted.
- Act as a key translator between technical teams and business stakeholders, making complex AI/modeling concepts accessible and showing their practical implications for deals, policies, and risk posture.
- Build and maintain a backlog of transformation initiatives, with clear prioritization, business cases, and expected impact.
Is this opportunity right for you? We are looking for candidates who have:
- Bachelor’s degree in quantitative or business field, or equivalent experience
- 5\+ years of experience in Merchant Underwriting, Commercial Credit Risk, Counterparty Risk Management, or closely related domains in payments, acquiring, fintech, or lending.
- Demonstrated track record of owning and delivering underwriting or risk transformation programs (e.g., implementation of a decisioning platform, redesign of credit policies, automation of underwriting workflows) from concept to execution.
- Experience operating at a level above individual underwriting management, with responsibility for frameworks, tools, and strategies used by multiple teams or regions.
- Deep comfort working with data: you write your own queries, validate logic, and explore datasets directly rather than relying solely on BI reports.
- Hands on familiarity with risk/decisioning platforms (e.g., rule engines, credit decisioning systems, workflow/orchestration tools) and how they integrate with upstream application flows and downstream servicing.
- Experience working alongside data science/ML teams: defining features, success metrics, and evaluation criteria for models used in underwriting or risk monitoring.
- Experience implementing or significantly enhancing an automated underwriting or decisioning platform in a payments, fintech, acquiring, or lending environment.
- Familiarity with common payment processing stacks (gateways, acquirers, processors), chargeback patterns, and high risk merchant categories.
- Exposure to fraud and transaction risk tools, and understanding of how to integrate fraud insights into credit/underwriting strategies.
- Practical experience designing and deploying AI or ML supported workflows in a production risk or underwriting context.
- Understanding of Generative AI and LLM capabilities and limitations, including prompt design, retrieval augmented generation, summarization, and classification of unstructured documents.
- Ability to design end to end GenAI enabled processes (e.g., document ingestion extraction summarization risk scoring human review) and measure their impact on efficiency and quality.
- Familiarity with model governance, explainability, and control frameworks to ensure AI driven decisions remain transparent, auditable, and policy aligned.
- Ability to see the underwriting function as a system: policies, data, tools, processes, and people, and to identify leverage points where change will have outsized impact.
- Strong product thinking mindset: user centric approach to designing tools and workflows that underwriters and stakeholders actually adopt and value.
- Comfort building business cases (baseline metrics, projected impact, resource needs) to prioritize transformation initiatives and secure buy in.
- Excellent written and verbal communication skills, with the ability to clearly articulate proposed changes, trade offs, and expected outcomes to technical and non technical audiences.
- Builder and change agent mentality: you identify gaps, propose pragmatic solutions, run pilots, iterate quickly, and push through ambiguity and resistance.
- Ability to read, write, speak and understand the English language in a business environment
CSGer Perks \& Benefits
- Work from Home
- Employee Belonging Groups
- Healthcare: Dental, Medical, and Vision
- Paid Vacation, Volunteer, and Holiday Time Off
- And so much more!
\#LI\-Remote
\#LI\-CA1
Please submit your application at csgi.com/careers. Applications will be accepted for at least 5 days from original posting date.
Colorado Residents: In any materials you submit, you may redact or remove age\-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.
Position Pay Range:
*This range represents the low and high end of the salary range for this position. Actual salaries will vary based on factors including but not limited to geographical location and experience.*
$114,677\.02\-$183,479\.40
This role is eligible for a bonus opportunity.
Location(s):
United States RemoteAccommodation:
If you would like to be considered for employment opportunities with CSG and need special assistance due to a disability or accommodation for a disability throughout any aspect of the application process, please call us at \+1 (402\) 431\-7440 or email us at accommodations@csgi.com. CSG provides accommodations for persons with disabilities in employment, including during the hiring process and any interview and/or testing processes.
Our Guiding Principles:
Impact: Always help and empower others, whether they’re colleagues or customers. When our employees set their minds to something, great things happen.
Integrity: Do what’s right for our customers and our people while being authentic. We treat everyone with trust and respect—that’s just who we are.
Inspiration: Be bold in the way you think and passionate about the work you do. Test out innovative ideas without the fear of failure.
Our Story:
--------------
CSG empowers companies to build unforgettable experiences, making it easier for people and businesses to connect with, use and pay for the services they value most. For over 40 years, CSG's technologies and people have helped some of the world's most recognizable brands solve their toughest business challenges and evolve to meet the demands of today's digital economy.
By channeling the power of all, we make ordinary customer and employee experiences extraordinary. Our people \[CSGers] are fearlessly committed and connected, high on integrity and low on ego, making us the easiest company to do business with and the best place to work. We power a culture of integrity, innovation, and impact across our locations, representing the most authentic version of ourselves to build a better future together. That's just who we are.
*Learn more about CSG Inclusion \& Impact here.*
*Our culture is award\-winning: CSG has been recognized a US News \& World Report “Best Companies to Work For” for 2025\-2026, a Newsweek “America’s Greatest Workplaces in Tech” for 2025 and “The Top Company in Technology for Women to Work” for 2025, among many others.*
Salary Context
This $114K-$183K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At CSG, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($149K) sits 11% below the category median. Disclosed range: $114K to $183K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
CSG AI Hiring
CSG has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $183K - $183K.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.