M
$115,000 – $184,000
Listed on Mastercard’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This role leads the design and deployment of large language model systems, agents, and retrieval-augmented generation pipelines at enterprise scale. It suits experienced ML engineers who have built production generative AI applications and want to architect next-generation AI systems within a global fintech organization.
Our summary, not Mastercard’s wording. The full posting is on their site.
Skills this role names
- AWS SageMaker
- BERT
- Clip Studio Paint
- CrewAI
- Databricks
- Falcon
- FastAPI
- Gemini
- Hugging Face
- LangChain
- LangGraph
- LLM Fine-Tuning
- MLflow
- Neo4j
- Pinecone
- Python
- PyTorch
- SQL
- TensorFlow
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What they ask for
Required
- Master's or Bachelor's degree in Computer Science, AI/ML, or Engineering
- Significant hands-on experience leading complex Gen AI or ML engineering programs in production
- Expert-level experience designing and deploying LLM applications, agentic systems, and RAG pipelines from prototype to production
- Deep proficiency with LLM ecosystems including OpenAI, Anthropic, Gemini, Hugging Face, and open-source foundation models
- Strong command of Gen AI engineering patterns including prompt engineering, chain-of-thought reasoning, tool calling, and vector embeddings
- Applied knowledge of ML fundamentals including predictive modeling and deep learning
- Excellent Python engineering skills including async programming, API development, and microservices
- SQL proficiency
- Hands-on experience with cloud AI infrastructure on AWS, Azure, or GCP
- Strong analytical, communication, and stakeholder management skills
Nice to have
- Familiarity with MLOps/LLMOps tooling beyond MLflow
- Experience with responsible AI and bias detection frameworks
- Knowledge of state and context management systems for agent reasoning