Senior AI Engineer

Description

Our Purpose

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Title and Summary

The Capabilities and Data team within Mastercard Services builds the foundational infrastructure that powers hundreds of customer-facing products. Some of the strongest and most useful capabilities are proprietary large language model ensembles that power the fundamental data operations of our analytics and insights solutions. In this role, you will develop and scale shared AI capabilities that support products and teams across Mastercard.

Our LLM-based entity resolution system is already in production, supporting multiple teams across Mastercard. We are now expanding it into an enterprise-level capability and need your help to improve its scalability, performance, and cost efficiency while making it easier to adopt and integrate across products.

We are looking for a hands-on AI engineer who combines strong software engineering skills with experience bringing AI solutions into production. You should be comfortable tackling open-ended technical challenges, evaluating alternative approaches, and working independently. Working with platform, product, and operations teams, you will take solutions from design through deployment and ongoing operation.

Roles & Responsibilities:

Design and own the architecture for deploying and operating AI capabilities at scale, including APIs and MCP servers that enable integration with products, agents, and applications.

Develop high-performance Python backend services for LLM inference orchestration, async job handling, streaming responses, and distributed data workflows supporting high-throughput operations.

Establish robust LLMOps and MLOps pipelines on Databricks (AWS) using MLflow, feature stores, prompt evaluation frameworks, model lineage tracking, and continuous retraining workflows to ensure reliable AI delivery.

Apply traditional ML and statistical modeling (regression, clustering, forecasting, ensemble methods) in hybrid architectures alongside LLMs for interpretable, explainability-first decision systems.

Translate R&D outcomes into scalable, reliable production solutions, collaborating with researchers to integrate and validate new models and techniques while optimizing inference latency, throughput, and cost without compromising output quality.

Continuously research, evaluate, and productionize advancements in generative modeling, agentic AI, multimodal transformers, and frontier foundation models

Lead solutions through security and architecture reviews, production approvals, and deployment.

Collaborate with platform, product, and consumer teams to support integration, adoption, and engineering standards.

All About You:

Master's or Bachelor's degree in Computer Science, AI/ML, or Engineering, with significant hands-on experience leading and delivering complex Gen AI or ML engineering programs in production environments.

Expert-level, hands-on experience designing, building, and deploying LLM applications, agentic systems, and RAG pipelines — from prototype to production.

Experience building MCP servers, or equivalent depth in building and exposing tool interfaces for LLM and agent consumption, with a clear grasp of how an agent actually consumes what you publish.

Deep proficiency with LLM ecosystems: OpenAI, Anthropic, Gemini, Hugging Face, and open-source foundation models (LLaMA, Mistral, Falcon, etc.).

Strong command of Gen AI engineering patterns: prompt engineering, chain-of-thought reasoning, tool/function calling, vector embeddings, semantic search, and agent memory architectures.

Excellent Python engineering skills including async programming, API development and building inference-ready microservices; SQL proficiency required.

Hands-on experience with cloud AI infrastructure (Databricks, AWS SageMaker, Bedrock, Azure OpenAI, or GCP Vertex AI) and familiarity with MLOps/LLMOps tooling (MLflow, Weights & Biases, etc.).

Readiness to work inside enterprise production implementation procedures such as security and architecture reviews, change management, and controlled pipeline promotion, and to treat those gates as part of the engineering work.

A genuine experimentation habit, meaning you can take an open-ended goal, design a fair test, run it, read the result honestly even when it contradicts what you expected, and decide what to do next.

Ability to work independently on open-ended problems, defining clear approaches and driving solutions through to completion.

Clear written and verbal communication, with the ability to explain a cost or architecture trade-off to engineers and to non-technical stakeholders in the same conversation.

Experience with tools such as Git, Jira, Visual Studio Code, Github Copilot.

Corporate Security Responsibility

All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

Abide by Mastercard’s security policies and practices;

Ensure the confidentiality and integrity of the information being accessed;

Report any suspected information security violation or breach, and

Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.