About the job
We build the workflow and automation engines that power how work gets done across the ServiceNow Platform. Our engines process billions of workflow transactions every month across enterprise customers, and we're now extending that foundation to support AI-native, agentic workflows. This is a team that cares about getting the architecture right, not just shipping features. We spend real time on performance, reliability, and making sure the platform holds up as customers push more volume and more complexity through it.
Responsibilities
Lead architecture and technical direction for problem areas across the portfolio, working with product and engineering leads to figure out what's blocking progress and where the real opportunities are.
Build prototypes and reference implementations that prove out an architectural idea before a whole team commits to it.
Test ideas with real experiments and real data to shape the direction of the platform.
Help move existing workflow capabilities toward AI-native patterns including agentic execution, better use of context and data, and more autonomous flows.
Dig into cases where an agentic feature isn't delivering value to determine if it is the architecture, data, UX, or cost model and drive the fix.
Write down patterns and tradeoffs that work so other teams don't have to rediscover them.
Sit in on design reviews and tough calls across teams to help weigh shipping speed against long-term durability.
Qualifications
Minimum
15+ years of related engineering experience, or equivalent practical experience.
Strong experience building and operating large-scale distributed systems, workflow engines, integration frameworks, or cloud-scale infrastructure handling high transaction volume.
Deep understanding of system design for scale, reliability, observability, fault tolerance, data quality, security, and production readiness.
Strong technical depth in Java, Python, or similar programming languages.
Proven ability to define technical direction, influence architecture across teams, and translate ambiguous product strategy into executable engineering plans.
Ability to operate as a Principal IC by leading through technical credibility, collaboration, and influence rather than formal authority.
Strong judgment in balancing long-term architecture with pragmatic execution, customer impact, engineering velocity, and business priorities.
Experience mentoring senior engineers, raising engineering standards, and building reusable patterns that create leverage across teams.
Preferred
Experience with AI-powered products, automation platforms, AI-assisted workflows, or agentic systems.
Ability to reason about AI-assisted enterprise workflows, including data quality, workflow correctness, evaluation, observability, safety controls, and human-in-the-loop review.
Experience with workflow automation, business process management, low-code/no-code platforms, or process/task orchestration at scale.
Experience working with large-scale enterprise customers, production systems, and complex migration or modernization efforts.