Job Closed
Manager, Forward-Deployed AI Engineer (AI Mobilization & Transformation)
- Source: Workday
- Mastercard
- $161,000 to $266,000 /yr
- On-site
- New York City
- Full-Time
- Senior
Job Description
- 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
- 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
- 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
- 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
- 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
- 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- Demonstrated ability to build rela…
Skills
- mastercard
- azure-arc
- microsoft-azure
- Github
- gitHub-copilot
- claude-by-anthropic
- claude-code
- workday
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