Principal AI Platform Engineer

It all started when engineer Fred Luddy wrote code that automated a tedious task for his coworker, Phyllis. She cried tears of joy. That moment inspired Fred to build a company that could do that for everyone—freeing people from busywork so they could focus on meaningful work. Today, ServiceNow is the AI control tower for business reinvention. Our ServiceNow AI platform brings together any AI, any data, and any workflow— helping 85% of the Fortune 500® work smarter, faster, and better. We're building an AI-native culture where technology and talent are unstoppable together. And we're just getting started.

Join us to put AI to work for people.

The Principal AI Platform Software Engineer acts as the Forward Deployed Engineer (FDE) lead architect directing a group of FDEs in the CRM & Industry Engineering organization to design, build, ship, and operate autonomous intelligence systems — agent runtimes, reasoning pipelines, orchestration platforms, and AI-native applications. Across the full development and deployment lifecycle, the Principal FDE translates customer and product problems into precise specifications, directs autonomous and semi-autonomous agents to generate AI Agents, engineers the context and guardrails that make that output reliable, and verifies that the resulting systems are correct, secure, reliable, and maintainable.

• Act as the Lead Architect for a group of FDEs. Establish FDE best practices and reference architecture across the group of 30+ FDEs in the CRM & Industry Engineering organization deploying Agentic AI solutions across Case Management and Conversational channels like Voice and Chat.
• Translate customer use cases into precise specifications. Convert customer AI use cases into efficient Agentic AI solutions using the ServiceNow AI Platform and other AI tools.
• Orchestrate AI agents to build and refactor software. Decompose work into agent-sized tasks and direct AI coding agents and tools to generate, modify, and refactor production code across multiple files and services, supervising several workstreams in parallel.
• Engineer context for reliable agent output. Author and maintain the context that drives correct results — system prompts, agent configuration and instruction files, architectural decision records, glossaries, golden examples, and agent-readable documentation — and curate what information enters the model at the right level of detail.
• Verify and review output against intent. Rigorously evaluate code — human- or agent-generated — for correctness, spec adherence, security, performance, and maintainability, confirming behavior matches intent and that edge cases, failure modes, and concurrency are genuinely handled rather than superficially passing tests.
• Make architectural and design decisions under uncertainty. Determine what to build, in what sequence, and within what constraints before agents begin work, and decide when to delegate to an agent versus implement directly, ensuring solutions fit...

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