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About This Role
Job Description: We are seeking a highly skilled and experienced Machine Learning Modeling Lead \- Credit Modeling to join our ML Model Innovation team within the banking/fintech domain (digital lending). The ideal candidate will be responsible for leading the development and deployment of machine learning models that power key business decisions such as collections models, credit risk scoring, fraud detection, customer segmentation, and personalized financial services.
The Individual needs to have strong knowledge of banking business, data and domain across the customer lifecycle as well as bureau data. They will collaborate with cross\-functional teams and provide technical leadership to junior ML modelers and data scientists.
Key Roles and Responsibilities \-
- Lead end\-to\-end ML solution development \& innovation from data exploration, feature engineering, model development, validation, deployment, and monitoring.
- Develop robust models which can drive business benefits. Support and review junior scientist submissions and share enhancement suggestions
- Responsible for documentation/documentation reviews, model reviews and submission
- Responsible for managing queries raised by Validation teams for the model
- Collaborate with implementation teams to deploy models into production environments (cloud or on premises).
- Work closely with business stakeholders to translate banking domain challenges into data\-driven solutions.
- Guide junior data scientists and engineers on best practices in model development
- Continuously evaluate new tools, technologies, and frameworks relevant to ML in finance.
Publish internal research and promote a culture of innovation and experimentation.
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Candidate Profile:
- Strong business knowledge of banking analytics across the retail banking customer lifecycle.
- 12\+ years of experience in applied machine learning model development in the banking or financial services domain.
- Hands\-on experience leading ML projects and teams.
- Strong experience with model development, deployment and monitoring in production environments.
- Familiarity with collections, underwriting, fraud and ethical considerations in banking ML models.
- Demonstrable leadership ability, superior problem solving and people management skills
- Master’s or Similar in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field
Skills :
- Expert in Python, SQL, ML libraries (Numpy, Pandas, Scikit\-learn, TensorFlow, PyTorch) and techniques (Regression, Decision Trees, Ensembles: XGBoost, GBM, Random Forest, Unsupervised Learning, etc.).
- Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is added benefit.
- Strong grasp of statistical modeling, optimization, and deep learning techniques.
Excellent communication skills and ability to explain complex concepts to non\-technical stakeholders.
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What we offer:
- EXL Analytics offers an exciting, fast paced and innovative environment, which brings together a group of sharp and entrepreneurial professionals who are eager to influence business decisions. From your very first day, you get an opportunity to work closely with highly experienced, world class analytics consultants.
- You will learn effective teamwork and time\-management skills \- key aspects for personal and professional growth
- Analytics requires different skill sets at different levels within the organization. At EXL Analytics, we invest heavily in training you in all aspects of analytics as well as in leading analytical tools and techniques.
- We provide guidance/ coaching to every employee through our mentoring program wherein every junior level employee is assigned a senior level professional as advisors.
Sky is the limit for our team members. The unique experiences gathered at EXL Analytics sets the stage for further growth and development in our company and beyond.
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The typical base pay range for this role across the U.S. is USD $202,000 \- $280,000 per year.
For more information on benefits and what we offer please visit us at https://www.exlservice.com/us\-careers\-and\-benefits
The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale.
Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position.
The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher\-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.
- Responsibilities: Lead end\-to\-end ML solution development \& innovation from data exploration, feature engineering, model development, validation, deployment, and monitoring.
- Develop robust models which can drive business benefits. Support and review junior scientist submissions and share enhancement suggestions
- Responsible for documentation/documentation reviews, model reviews and submission
- Responsible for managing queries raised by Validation teams for the model
- Collaborate with implementation teams to deploy models into production environments (cloud or on\-premises).
- Work closely with business stakeholders to translate banking domain challenges into data\-driven solutions.
- Guide junior data scientists and engineers on best practices in model development
- Continuously evaluate new tools, technologies, and frameworks relevant to ML in finance.
- Publish internal research and promote a culture of innovation and experimentation.
Qualifications: Candidate Requirements:
- Strong business knowledge of banking analytics across the retail banking customer lifecycle.
- 12\+ years of experience in applied machine learning model development in the banking or financial services domain.
- Hands\-on experience leading ML projects and teams.
- Strong experience with model development, deployment and monitoring in production environments.
- Familiarity with collections, underwriting, fraud and ethical considerations in banking ML models.
- Demonstrable leadership ability, superior problem solving and people management skills
- Master’s or Similar in Computer Science, Data Science, Statistics, Applied Mathematics, or a related quantitative field
Skills :
- Expert in Python, SQL, ML libraries (Numpy, Pandas, Scikit\-learn, TensorFlow, PyTorch) and techniques (Regression, Decision Trees, Ensembles: XGBoost, GBM, Random Forest, Unsupervised Learning, etc.).
- Knowledge of MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) is added benefit.
- Strong grasp of statistical modeling, optimization, and deep learning techniques.
Excellent communication skills and ability to explain complex concepts to non\-technical stakeholders.
*
What we offer:
- EXL Analytics offers an exciting, fast paced and innovative environment, which brings together a group of sharp and entrepreneurial professionals who are eager to influence business decisions. From your very first day, you get an opportunity to work closely with highly experienced, world class analytics consultants.
- You will learn effective teamwork and time\-management skills \- key aspects for personal and professional growth
- Analytics requires different skill sets at different levels within the organization. At EXL Analytics, we invest heavily in training you in all aspects of analytics as well as in leading analytical tools and techniques.
- We provide guidance/ coaching to every employee through our mentoring program wherein every junior level employee is assigned a senior level professional as advisors.
Sky is the limit for our team members. The unique experiences gathered at EXL Analytics sets the stage for further growth and development in our company and beyond.
*
The typical base pay range for this role across the U.S. is USD $200,000 \- $280,000 per year.
For more information on benefits and what we offer please visit us at https://www.exlservice.com/us\-careers\-and\-benefits
The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale.
Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position.
The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher\-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.
Salary Context
This $200K-$280K range is above the 75th percentile 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 EXL Service, 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 ($240K) sits 12% above the category median. Disclosed range: $200K to $280K.
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.
EXL Service AI Hiring
EXL Service has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span San Francisco, CA, US, FL, US, US. Compensation range: $125K - $300K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% above the national 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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