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Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, were 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 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 .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.