MastercardSenior AI Engineer
Mastercard is hiring a Senior AI Engineer in Prague, Czechia. Level rates it ; you can apply on Mastercard.
Level's read on this job
- Level rates this AI engineer role Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include OpenAI, Anthropic and Gemini.
- Level classifies it as a senior role.
- Level lists 1,000+ other open roles at Mastercard.
- The role was posted in the last few days.
AI in this role
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
Senior AI EngineerThe 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.
How we rate this
Senior AI Engineer at Mastercard rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in AI engineer jobs at this level
Pay
- Typical AI engineer job
- $183k
This posting doesn't list pay. Use the typical AI engineer figure when pay comes up. See AI engineer jobs
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- What's a project where you used OpenAI hands-on?
- Walk me through how you've used Anthropic in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Prompt engineering, RAG, ML Ops, OpenAI, and Anthropic. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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