Senior AI Engineer

Mastercard

Confirmed live today High trust

Quick summary

Work type: On-site
Location: O Fallon, MO
Salary: $115,000–$184,000 / yr
Posted: 57 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $195k

This role $150k

$101k $250k

below market most similar roles pay here above market

This role pays less than 80% of similar roles. Most pay $155,000–$235,750 — the blue band above. At the midpoint, this role pays about $150k versus about $195k 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 150 open roles on FindRole.

Listed pay typically runs $122,000–$207,000 across 142 roles with salary data.

Most-posted roles

View all roles at Mastercard

At a glance

TL;DR · Senior AI Engineer

Role Posting Log in to save

The Senior AI Engineer joins the Business & Market Insights group within the Operational Intelligence Program to architect and deliver next-generation generative AI systems. This role involves building multi-agent systems, multimodal generative AI pipelines, and production-grade RAG or Graph-RAG systems to transform complex data into agentic applications. Key responsibilities include leading LLM fine-tuning, establishing LLMOps pipelines, and developing high-performance Python backend services for inference orchestration. The candidate will utilize technologies including LangGraph, CrewAI, AutoGen, Pinecone, Neo4j, and AWS Neptune, alongside models like GPT-4o, Gemini, and LLaMA. Expertise in PyTorch, TensorFlow, FastAPI, and Databricks is required to solve business problems related to autonomous reasoning, semantic retrieval, and responsible AI governance, ensuring scalable, production-grade deployment of frontier foundation models for enterprise-scale performance.

What does a AI Engineer earn?

Median $211200 from 139 postings across 39 companies.

See salary data

Skills

Python LLM RAG LangGraph CrewAI AutoGen LangChain PyTorch TensorFlow MLflow Databricks AWS SageMaker Bedrock Pinecone pgvector OpenSearch Neo4j AWS Neptune FastAPI SQL Hugging Face LoRA PEFT RLHF

What you'll do

Architect and lead the development of multi-agent AI systems using frameworks like LangGraph and CrewAI.
Design and operationalize multimodal generative AI pipelines using transformer-based architectures for text, image, and graph data.
Build production-grade RAG and Graph-RAG systems integrating vector databases and knowledge graphs.
Lead LLM fine-tuning, prompt engineering, and model alignment strategies including RLHF and LoRA.
Establish robust LLMOps and MLOps pipelines on Databricks using MLflow and continuous retraining workflows.
Develop high-performance Python backend services for LLM inference orchestration and distributed data workflows.
Implement Responsible AI practices including bias detection, hallucination mitigation, and output safety guardrails.
Apply traditional ML and statistical modeling in hybrid architectures for interpretable decision systems.

What we're looking for

Bachelor's or Master's degree in Computer Science, AI/ML, or Engineering.
Significant hands-on experience leading and delivering complex Gen AI or ML engineering programs in production environments.
Expert-level experience designing, building, and deploying LLM applications, agentic systems, and RAG pipelines from prototype to production.
Deep proficiency with LLM ecosystems including OpenAI, Anthropic, Gemini, Hugging Face, LangChain/LangGraph, and open-source foundation models.
Strong command of Gen AI engineering patterns such as prompt engineering, chain-of-thought reasoning, tool calling, and agent memory architectures.
Solid applied knowledge of ML fundamentals including predictive modeling, deep learning (PyTorch, TensorFlow), and statistical techniques.
Excellent Python engineering skills including async programming, API development (FastAPI), and SQL proficiency.
Hands-on experience with cloud AI infrastructure (AWS SageMaker, Bedrock, Azure OpenAI, or GCP Vertex AI) and MLOps/LLMOps tooling.

Save