Director ForwardDeployed AI Engineer AI Mobilization Transformation
Listed on 2026-07-16
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Software Development
AI Engineer (Applied/Software)
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.
Titleand Summary
Director, Forward‑Deployed AI Engineer - AI Mobilization & Transformation
OverviewThe Director, Forward‑Deployed AI Engineer serves as Mastercard's embedded AI transformation leader, partnering directly with business units to identify high‑value opportunities, develop production‑grade AI solutions, and mobilize teams to adopt new ways of working.
Reporting to the VP of Organizational Readiness, this role combines deep technical expertise with change leadership. Rather than building solutions in isolation, you will work alongside business teams to solve real problems, demonstrate the art of the possible, and develop internal capability through hands‑on engagement.
Success is measured not only by the solutions delivered, but by the number of leaders, engineers, analysts, and teams equipped to independently leverage AI, agents, and multi‑agent systems in their daily work.
The Role Mobilizing AI Adoption Through Bespoke Engagements- Embed within business units to identify strategic workflow, productivity, and decision‑making opportunities where AI can create measurable value.
- Lead AI Transformation Engagements that combine discovery, solution design, implementation, and capability building.
- Build high‑impact use cases that serve as showcase examples for broader organizational adoption.
- Translate business challenges into practical applications of AI, agents, and multi‑agent orchestration.
- Create reusable playbooks, patterns, and training assets that accelerate adoption across the enterprise.
- Partner with business leaders to demonstrate measurable outcomes and establish local AI champions.
- Develop repeatable transformation approaches that can be scaled across multiple business units and functions.
- Identify and prioritize high‑value opportunities that accelerate enterprise AI adoption and capability growth.
- Design and deploy production‑ready AI assistants, agents, and orchestration frameworks that solve real business problems.
- Use each engagement as a live learning environment where business and technical teams learn modern AI practices through delivery.
- Coach engineers, analysts, product managers, and operational teams on AI‑first ways of working.
- Establish a "train‑the‑trainer" model that enables local teams to continue scaling capabilities after engagements conclude.
- Facilitate hands‑on workshops focused on prompt engineering, agent design, workflow automation, Copilot practices, and AI‑assisted development.
- Develop internal champions capable of independently driving AI adoption and solution delivery.
- Promote knowledge sharing and adoption of best practices across teams and business units.
- Architect and implement solutions leveraging Copilot Studio, Azure AI, agent frameworks, orchestration systems, and enterprise platforms.
- Develop multi‑agent solutions that automate complex business processes and decision flows.
- Introduce modern engineering practices including AI‑assisted software development, evaluation frameworks, observability, and governance.
- Establish proven reference architectures and patterns that can be replicated across business units.
- Help business teams evolve from experimentation to operationalized AI solutions.
- Partner with engineering and business leaders to drive scalable adoption of agentic solutions across the enterprise.
- Evaluate emerging AI capabilities and identify opportunities to apply them to business challenges.
- Document emerging patterns, successful use cases, implementation approaches, and lessons learned.
- Build an enterprise library of AI‑enabled workflows, agents, and…
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