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
AI Engineer ||
Role Summary The AI Engineer is responsible for designing and delivering AI-powered solutions that improve engineering productivity and business workflows. This role requires a strong foundation in AI/ML concepts, with hands-on expertise in LLM systems, data preparation, and context design (including chunking) to build scalable, high-quality AI solutions.
What You Will Do
Lead AI-Driven Solution Development
- Identify and drive high-impact AI use cases across engineering and business workflows
- Design end-to-end AI solutions leveraging LLMs, orchestration, and automation
- Move rapidly from exploration to production-grade implementations
Build LLM Solutions & Intelligent Agents
- Develop LLM-based applications including prompt frameworks, orchestration layers, and integrations
- Create and evolve AI-powered agents (e.g., Copilot-based or similar) for workflow automation and decision support
- Apply techniques such as retrieval-augmented approaches, context management, and prompt optimization
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Design Data, Context & Chunking Strategies (Critical)
- Structure and prepare data for LLM consumption, including effective chunking and context window optimization
- Design retrieval strategies that improve relevance, accuracy, and response quality
- Balance context size, latency, and cost while maintaining solution effectiveness
Apply Strong AI/ML Foundations
- Understand and reason about model behavior, evaluation, bias, and limitations
- Tune and adapt prompts, context, and workflows instead of relying on model retraining
- Ensure responsible and effective use of AI through sound engineering practices
Drive Engineering Productivity with AI
- Apply AI tools (e.g., Copilot and similar) to improve developer velocity and quality
- Build reusable patterns for AI-assisted coding, testing, and documentation
- Measure and scale productivity impact across teams
Data & Application Engineering
- Build data pipelines and integrations to support AI workflows
- Develop APIs, services, and applications embedding AI capabilities
- Ensure solutions are scalable, maintainable, and integrated into real workflows
