Lead Forward Deployed Engineer (AI)

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

Lead Forward Deployed Engineer (AI)

About the Team:
Mastercard's Business & Market Insights (B&MI) group empowers organizations to achieve growth and innovation goals by providing unparalleled data-driven insights and advanced analytics. By leveraging proprietary data and global expertise, B&MI helps businesses make smarter, more informed decisions that drive profitability and success. We turn complex data into actionable strategies that lead to better outcomes and sustained competitive advantage.

We are currently looking for a Lead Forward Deployed Engineer (AI) for the Operational Intelligence Program within the B&MI group. This role exists at the boundary between customer and product - /ou embed with clients before a single line of production code is written, validate whether an AI solution actually solves the business problem, and hand a scoped, evidence-backed brief to the product and engineering teams that build it. You are a builder first, but your deliverable is clarity, not code.


Roles and Responsibilities:
- Embed directly with issuer and acquirer clients to understand their operational workflows, surface pain points, and identify where AI-driven automation or intelligence creates measurable business value.
- Design and build rapid proof-of-concept prototypes - agentic pipelines, conversational interfaces, analytical tools - scoped deliberately to test a specific business hypothesis, not to be production-ready.
- Translate ambiguous client requirements into concrete, testable problem statements; push back on requirements that are too broad to validate or too narrow to generalize.
- Run structured validation sessions with customers using working prototypes, gather qualitative and quantitative signal on whether the PoC addresses the stated need, and document what it does not.
- Synthesize field observations into product briefs: scoped requirements, edge cases discovered in the wild, constraints the platform team would not have known from a desk, and a clear go/no-go recommendation.
- Partner with product managers to ensure PoC findings directly inform roadmap decisions - requirements that survive validation become backlog items; those that do not get killed before investment is made.
- Maintain a working knowledge of the AI capability landscape (LLMs, agentic frameworks, retrieval-augmented generation, structured data reasoning) broad enough to know what is feasible in a six-week PoC versus
what requires a platform build.
- Identify patterns across client engagements that suggest platform-level investment - repeatable needs that should become product features rather than one-off prototypes.
- Represent technical feasibility in pre-sales and discovery conversations; give customers and internal stakeholders an honest read on what AI can and cannot do for their specific workflow today.
- Mentor engineers and analysts on the team on customer discovery craft - how to ask questions that reveal unstated requirements, how to scope a PoC to answer one question well, and how to present findings without overselling.

All About You:
- Master's or bachelor's degree in Computer Science, Engineering, or a related field, with significant experience delivering AI or data solutions to enterprise clients.
- Proven track record building working AI prototypes that directly influenced product or investment decisions - you can point to cases where your PoC changed what got built.
- Strong engineering foundation: you can write clean, working code quickly without over-engineering for production concerns that are irrelevant at the PoC stage.
- Fluent in Python and comfortable with SQL; experience with large-scale structured data and cloud data platforms is a strong plus.
- Hands-on experience with LLM APIs and agentic patterns - you understand the tradeoffs between what you can prompt, what you need to engineer, and what requires fine-tuning.
- Exceptional customer-facing communication: you can run a discovery session with a non-technical reconciliation analyst in the morning and write a technical scoping document in the afternoon.
- Strong product instinct: you can distinguish a business requirement from a solution assumption, and you know when a client is describing a symptom rather than a root cause.
- Comfort operating in ambiguity - you thrive when the problem statement is unclear and see scoping it as part of the job, not a blocker.
- Experience working in regulated industries (financial services, payments, healthcare) where data sensitivity, compliance constraints, and customer trust shape what can and cannot be prototyped.
- Track record of handing off well-scoped, evidence-backed briefs to product and engineering teams; you measure success by what gets built from your findings, not by the prototype itself.

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.