Senior AI Engineer
Mastercard3 days ago
Prague, CzechiaSenior
Responsibilities
- Design and own architectures for deploying and operating AI capabilities at scale, including APIs and MCP servers.
- Develop high-performance Python backend services for LLM inference orchestration, asynchronous job handling, streaming responses, and distributed data workflows.
- Establish LLMOps and MLOps pipelines using Databricks, MLflow, feature stores, prompt evaluation, model lineage, and continuous retraining.
- Apply traditional machine learning and statistical modeling methods in hybrid architectures alongside LLMs.
- Translate research outcomes into scalable production systems while optimizing inference latency, throughput, cost, and output quality.
- Research and productionize generative modeling, agentic AI, multimodal transformers, and foundation-model advancements.
- Lead security reviews, architecture reviews, production approvals, and deployments.
- Collaborate with platform, product, operations, research, and consumer teams to support integration and adoption.
Requirements
- Bachelor’s or master’s degree in Computer Science, AI/ML, or Engineering.
- Significant hands-on experience leading and delivering complex generative AI or machine learning engineering programs in production.
- Expert experience designing, building, and deploying LLM applications, agentic systems, and RAG pipelines from prototype through production.
- Experience building MCP servers or equivalent tool interfaces for LLM and agent consumption.
- Deep proficiency with OpenAI, Anthropic, Gemini, Hugging Face, and open-source foundation models such as LLaMA, Mistral, and Falcon.
- Strong command of prompt engineering, chain-of-thought reasoning, tool and function calling, vector embeddings, semantic search, and agent memory architectures.
- Excellent Python engineering skills, including asynchronous programming, API development, and inference-ready microservices, plus required SQL proficiency.
- Hands-on experience with Databricks, AWS SageMaker, Amazon Bedrock, Azure OpenAI, or Google Cloud Vertex AI.
- Familiarity with MLOps and LLMOps tools such as MLflow and Weights & Biases.
- Ability to work through security and architecture reviews, change management, and controlled pipeline promotion.
- Ability to independently solve open-ended problems, conduct fair experiments, and communicate technical trade-offs to technical and non-technical stakeholders.
- Experience with Git, Jira, Visual Studio Code, and GitHub Copilot.
Categories
About Mastercard
Mastercard builds and operates a global payments network used by banks, merchants, fintechs, and governments, offering card processing, real-time payments, tokenization, and fraud/risk services. It generates revenue from transaction processing and assessment/service fees across more than 200 countries and territories. Founded in 1966 and headquartered in Purchase, New York, Mastercard is a public company listed on the NYSE (ticker: MA).