Role
The Principal Forward Deployed Engineer is a senior individual contributor who deploys into Services teams to deliver working software and durable capability. Engagements are deliberately fluid. Some last few weeks to help a team ship a critical feature or clear a bottleneck, while others run longer to stand up a capability a program is missing. In every engagement you write production code, pair with the team’s engineers, put AI to work in how they build, and leave the team faster and more capable than you found it.
Deliver Where It’s Needed Most
• Embed directly into Services product and engineering teams, writing production-quality code and shipping features alongside them.
• Provide surge engineering capacity during critical delivery spikes, helping programs accelerate roadmaps and hit high-stakes milestones without permanently expanding headcount.
• Diagnose and clear technical bottlenecks across architecture, performance, integration, tooling, and delivery process.
• Fill short-term gaps with deep expertise, standing up new capabilities, and transferring them cleanly to the product team.
• Move fluidly across Services as priorities shift, scoping each engagement with program leaders and Developer Enablement so effort lands where it matters most.
Engineer AI Into How Services Builds
• Design and deploy production-grade AI solutions such as assistants, agents, and multi-agent workflows that solve real problems inside Services programs.
• Apply modern AI engineering practices, including LLMs, RAG, and agent frameworks, along with the evaluation, observability, and governance needed to run them safely in production.
• Mature AI adoption across the software development lifecycle, including AI-assisted coding, testing, code review, and release.
• Evaluate emerging AI capabilities and turn them into reusable patterns and reference implementations that Services teams can adopt.
Upskill Teams and Scale What Works
• Coach teams on AI-first ways of working through hands-on delivery, including pairing, prompt engineering, and agent design.
• Leave every team more self-sufficient than you found it, emphasizing documentation and knowledge transfer, so programs do not stay dependent on you.
• Carry patterns, friction points, and reusable solutions back from the field to inform Services-wide enablement, tooling, and golden paths.
• Partner with enterprise architecture, platform, and developer enablement teams so what you deliver aligns with Mastercard standards and scales beyond a single program.
Your impact shows up in the teams you leave behind. The programs you support ship faster, more of Services builds with AI because you showed them how, and the engineers you coach can keep building after you move on. You also leave behind reusable AI patterns and reference implementations that other teams can pick up and run with.