Senior AI Engineer

Mastercard

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
O Fallon, MO
Salary
$115,000–$184,000 / yr
Posted
3 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $204k
This role $150k
$99k most similar roles pay here $262k

This role pays less than 83% of similar roles. Most pay $162,000–$246,150 — the shaded band above. At the midpoint, this role pays about $150k versus about $204k for comparable roles.

Based on 240 similar postings.

Employer

About Mastercard

Mastercard is a global technology company in the payments industry, processing transactions between financial institutions and merchants using its extensive network of credit, debit, and prepaid card products. Industry: Payments Technology & Financial Services

Mastercard currently has 126 open roles on FindRole.

Listed pay typically runs $122,000–$207,000 across 112 roles with salary data.

Most-posted roles

View all roles at Mastercard

At a glance

TL;DR · Senior AI Engineer

The Senior AI Engineer joins the Technology Regulatory Market Compliance team to design, develop, and deploy AI-enabled solutions that transform how the organization manages regulatory, customer, compliance, and risk management activities. This hands-on role involves building production-ready applications using generative AI, large language models, retrieval-augmented generation, and vector databases to modernize processes like regulatory examinations and risk assessments. The candidate will manage the full lifecycle of AI products, including model training, fine-tuning, prompt engineering, and MLOps practices such as CI/CD pipelines and performance monitoring. Required skills include Python, machine learning frameworks like PyTorch or TensorFlow, and experience with cloud-native services. The role focuses on solving complex problems in highly regulated environments by automating evidence collection and ensuring that all AI solutions meet strict enterprise governance, security, and compliance standards.

What does a AI Engineer earn?

Median $211200 from 138 postings across 37 companies.

See salary data

What you'll do

  • Design and develop production-ready AI applications for regulatory examinations, risk assessments, and compliance activities.
  • Build and optimize generative AI solutions using LLMs, retrieval-augmented generation (RAG), and vector databases.
  • Manage the full MLOps lifecycle including automated testing, deployment pipelines, model versioning, and performance monitoring.
  • Implement prompt engineering, hyperparameter optimization, and guardrails to ensure secure and explainable AI outcomes.
  • Automate manual workflows for evidence collection, risk monitoring, and reporting processes.
  • Deploy scalable AI solutions across public and private cloud environments using enterprise-approved services.
  • Ensure all AI products comply with corporate security standards, regulatory requirements, and responsible AI principles.

What we're looking for

  • Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Information Technology, or a related technical discipline.
  • 5+ years of experience developing software, machine learning, artificial intelligence, advanced analytics, or automation solutions.
  • 3+ years of experience building and deploying enterprise AI or machine learning solutions in production environments.
  • Hands-on experience developing AI solutions using Python and modern AI/ML frameworks.
  • Experience with machine learning, generative AI, large language models, prompt engineering, and AI application development.
  • Experience building cloud-based applications and services in enterprise environments.
  • Experience implementing monitoring, observability, testing, deployment, and operational support capabilities for AI solutions.
  • Experience supporting highly regulated industries, compliance programs, risk management, cybersecurity, or technology controls (preferred); experience with MLOps platforms, PyTorch/TensorFlow, RAG, or vector search (preferred).

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