Principal AI Platform Engineer

ServiceNow•Santa Clara, CA

About The Position

Despite the listed title, the posting describes the role as Principal Forward Deployed Engineer within ServiceNow’s CRM & Industry Engineering group. This is a senior, customer-facing engineering role focused on turning agentic AI ambitions into production solutions for customer workflows, including sales automation, CPQ, customer service, and field service. The role leads the group’s technical direction, establishes reusable architectures and deployment patterns, and owns the correctness, reliability, and business outcomes of delivered agentic systems. The engineer works directly with customer business and technical teams, spending most of their time on discovery, solutioning, and deployment. They translate customer needs into well-scoped use cases, design and deploy agentic AI solutions on the ServiceNow platform, and advise customers on capabilities, limitations, tradeoffs, and responsible adoption. They also verify system behavior, security, and reliability; mentor engineers; and relay field learnings to ServiceNow product and engineering teams. The position offers access to senior customers and executives and the opportunity to define how agentic AI is deployed at scale. The posting lists a base pay range of $221,200–$387,100, plus equity when applicable, variable or incentive compensation, and benefits. Compensation varies by qualifications and work location.

Requirements

Demonstrated record of designing, deploying, and operating production software and AI solutions in customer-facing settings, including leading complex engagements to successful business outcomes.
Extensive experience working directly with customers to analyze needs, architect solutions, and deploy them to production in a forward-deployed, professional services, solution architecture, or comparable role.
Hands-on experience designing and operating autonomous agent systems, including planning, tool invocation, memory, context design, and guardrails.
Working command of LLM behavior, evaluation, and failure modes sufficient to reason about and verify agentic solutions in production.
Expert-level system design and solution architecture skills, including end-to-end design across systems and data, sound decisions under uncertainty, and judgment about what to build, buy, or configure.
Working knowledge of AI-integrated system security and reliability risks, and how to address them through least-privilege practices, monitoring, and rollback.
Excellent communication and stakeholder skills, including running discovery and working sessions and communicating with customer technical and executive audiences.
Record of leading and coaching engineers, including senior engineers, and establishing reusable standards across teams.
High autonomy as a recognized technical authority: ability to set multi-engagement strategy and architecture standards, drive an agenda aligned with organizational goals, and navigate ambiguity.
Bachelor’s degree in computer science, software engineering, or a related technical field, or equivalent practical experience.
15 years of related experience with a bachelor’s degree in computer science; or 12 years with a master’s degree; or a Ph.D. with 8 years of experience; or equivalent experience.

Nice To Haves

Knowledge of the ServiceNow platform and CRM & Industry domains, including sales automation, CPQ, customer service, and field service.
Advanced degrees or relevant certifications.

Responsibilities

Analyze customer processes, data, and goals and translate ambiguous problems into scoped agentic AI use cases with clear success criteria.
Architect agentic AI solutions on the ServiceNow platform, including agent workflows, integrations, data and context design, and guardrails.
Build, configure, integrate, validate, and deploy solutions into customer production environments.
Set technical direction, reference architectures, reusable deployment patterns, and solution accelerators for the FDE group.
Advise customers on agentic AI capabilities, limitations, tradeoffs, risks, and responsible adoption.
Verify that deployed agents are correct, secure, and reliable; test edge cases and failure modes, address risks such as prompt injection and data leakage, and establish monitoring and rollback for behavioral drift.
Provide architectural guidance, reviews, mentorship, and technical problem-solving across the group’s engagements.
Feed recurring use cases, platform gaps, and customer learnings back to ServiceNow product and engineering teams.