Title and Summary

Manager, Forward-Deployed AI Engineer - AI Mobilization & Transformation

Overview
The Manager, 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 Director, Forward-Deployed AI Engineer - AI Mobilization & Transformation, 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.
• Support the identification and delivery of high-value AI opportunities across business functions.
• Contribute reusable assets and implementation patterns that accelerate future engagements.

Building While Teaching
• 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, knowledge workers, 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 practitioners capable of independently applying AI tools and techniques within their teams.
• Promote knowledge sharing and adoption of established AI best practices.

Advancing Agentic Transformation
• 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.
• Apply established AI patterns and frameworks to accelerate solution delivery and adoption.
• Evaluate emerging AI capabilities and assist in translating them into practical business applications.

Capturing and Scaling Organizational Learning
• Document emerging patterns, successful use cases, implementation approaches, and lessons learned.
• Build an enterprise library of AI-enabled workflows, agents, and transformation stories.
• Identify adoption barriers and design interventions that accelerate organizational readiness.
• Contribute to enterprise readiness metrics by measuring adoption, productivity gains, capability growth, and business impact.
• Create a feedback loop between field engagements, engineering teams, and organizational readiness programs.
• Capture reusable assets, implementation approaches, and best practices from engagements.
• Share lessons learned to improve future AI transformation efforts across the organization.

All About You
• Extensive software engineering experience with a track record of building and deploying production-grade systems.
• Deep experience with AI technologies including LLMs, agent frameworks, RAG architectures, orchestration patterns, and AI application development.
• Experience building and deploying enterprise AI solutions that deliver measurable business outcomes.
• Strong facilitation and coaching abilities, with experience educating technical and non-technical audiences.
• Comfortable working directly with business stakeholders to identify opportunities and redesign workflows.
• Proven ability to influence organizational change through hands-on partnership and delivery.
• Experience mentoring and developing technical talent through real-world project engagements.
• Strong understanding of responsible AI, governance, risk management, and production monitoring practices.
• Ability to translate complex technical concepts into practical business value and adoption strategies.
• Experience supporting cross-functional initiatives that combine AI adoption, workflow transformation, and capability development.
• Demonstrated ability to build relationships and influence stakeholders across technical and business teams.
• Experience documenting and sharing repeatable patterns, practices, or implementation approaches.
• Strong communication skills with the ability to explain AI concepts to both technical and non-technical audiences.
• Experience driving adoption of new technologies through hands-on engagement and coaching.
• Experience with Copilot Studio, Azure AI, GitHub Copilot, Claude Code, or equivalent platforms preferred.
• Financial services, payments, or enterprise transformation experience preferred.
• Passion for developing others and creating sustainable capability within organizations.

Success in This Role
Success is not measured by the number of agents you build. Success is measured by the number of teams that can build without you.
You leave behind:
• New organizational capability.
• Repeatable AI patterns.
• Trained champions and practitioners.
• Demonstrated business value.
• Sustainable adoption of AI-first ways of working.
• Increased confidence and proficiency in applying AI across day-to-day work.
• Reusable assets and implementation practices that accelerate future adoption.

Pay Ranges
Purchase, New York: $161,000 - $266,000 USD

Arlington, Virginia: $161,000 - $266,000 USD

Atlanta, Georgia: $140,000 - $231,000 USD