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Tier-1 brand, popular AI title, mid-level scope, and metro context increase candidate competition.
Highly specialized GenAI and MLOps skills reduce cross-industry transferability and increase domain dependency.
Role mandates specific LLM, MLOps, cloud, and Responsible AI expertise, enforcing strict screening filters.
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Design, develop, and deploy scalable AI and Generative AI solutions including Large Language Models (LLMs), Agentic AI systems, and Retrieval-Augmented Generation (RAG) frameworks to improve decision-making and operational efficiency in Mastercard's Global Pricing & Interchange team.
Build and maintain AI infrastructures such as AI agent architectures, orchestration frameworks, backend services, APIs, and cloud-native AI/ML pipelines ensuring scalability, security, and governance compliance.
Collaborate with global stakeholders to identify AI opportunities, implement Responsible AI practices, and drive enterprise adoption of AI-powered solutions across pricing and interchange functions.
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, Data Science, or related technical field.
Experience designing, developing, and deploying AI, Generative AI, or machine learning solutions in production environments with hands-on work on LLMs, Agentic AI, and RAG architectures.
Proficiency in Python programming, API and microservice development, SQL, and working knowledge of cloud platforms such as Azure, AWS, Databricks, or Microsoft Fabric.
Work Experience Required: Not explicitly mentioned in the JD.
Has deep expertise with GenAI ecosystems and frameworks including OpenAI, Azure OpenAI, Gemini, Hugging Face, LangChain, and related tools relevant to building production-grade AI applications.
Experienced in AI solution lifecycle management including MLOps and LLMOps practices, Responsible AI implementation (bias monitoring, explainability, governance), and cloud-native AI deployments.
Strong capability to bridge technical AI concepts with business needs, effectively communicating across technical and non-technical stakeholders in complex, fast-paced financial services environments.