Principal Software Engineer (AI Control Tower)

• The AI Control Tower is ServiceNow’s central hub for governing every AI model, agent, and dataset across the enterprise. We’re building the infrastructure that enterprises will depend on as agentic AI moves from experiment to production
• We’re looking for a Principal Engineer who has built 1.0 products before: architecting from first principles, solving deep technical hurdles with early customers, and building technical foundations that compound adoption over time
• You’ll report to the Director of Engineering, AICT, and drive the technical architecture for critical product areas that will become standard infrastructure for how enterprises run AI at scale
• You are AI-native. You don’t just understand AI conceptually — you use it daily, you build with it, and you bring that instinct into how your team designs and ships
• Technical Architecture & Leadership Lead the architectural design and implementation of core AICT platform components. You’ll translate complex product requirements into scalable, observable technical plans and failure recovery patterns. You set the technical standard for how we build governance infrastructure
• 1.0 to Adoption Drive the full arc from early release to meaningful customer adoption. You understand that shipping a 1.0 is only half the job — the other half is learning fast, iterating based on what real customers do (not what they say), and building the feedback loops that compound adoption over time
• AI-Native Engineering Set the bar for how we use AI in engineering itself. From AI-assisted code review and automated testing to designing platform features that treat AI agents as first-class users, you lead by example in leveraging AI to improve both delivery velocity and software quality
• Mentorship & Technical Excellence Act as the strong technical voice who raises the bar around you. You run deep-dive architecture reviews and provide clear technical guidance. You represent AICT in technical forums and contribute to long-term platform strategy
• Cross-Functional Execution Work closely with Product Management, Design, and partner engineering teams to align on dependencies and ship coherent experiences. Represent engineering in planning and review forums with senior leadership

Benefits

• Generous family leave
• Matched donations
• Annual learning stipends
• Flexible PTO
• Competitive retirement plan
• Paid volunteer time- Distributed systems & event-driven architecture expertise, with focus on scalability, reliability, fault tolerance, and operational observability
• AI-native engineering practices: Proficiency across the full engineering lifecycle (architecture, implementation, debugging, security, CI/CD) using AI to drive delivery velocity and software quality
• Startup mindset: Direct experience building and shipping products from zero—ideally in high-growth environments where you were hands-on with both code and architecture decisions
• Hands-on mastery: Comfort operating at the highest architectural level while remaining deeply hands-on in code when it matters most
• Deep expertise in database schema design, data modeling, and modern data platforms, including relational, graph, vector, columnar, OLAP, and streaming data architectures, with the ability to design scalable data models for AI and enterprise workloads
• Technical influence without formal authority: Demonstrated ability to lead cross-team technical initiatives and drive architecture decisions through clear reasoning and credibility
• Deep technical foundation in cloud-native SaaS architecture: multi-tenant design, large-scale API design (REST/gRPC), Kubernetes orchestration, observability patterns, and multi-cloud platforms (AWS, Azure, GCP)
• 10+ years in software engineering, with a proven track record of shipping complex enterprise-grade SaaS products and managing their evolution through scale
• Governance, observability, or compliance product experience — understanding auditability, enterprise risk frameworks, and regulatory requirements
• Agent frameworks & production operations: Hands-on familiarity with agentic system challenges (tool reliability, context window management, failure modes, cost optimization)
• Enterprise security, IAM, or data governance background
• Familiarity with AI interoperability standards such as MCP, OpenAPI, or similar
• Advanced Degree: MS or PhD in Computer Science, AI, or a related technical field