Principal Software Engineer

ServiceNow•Santa Clara, CA

6h•$221,200 - $387,100

About The Position

The Principal Software Engineer will own the technical architecture of core components across ServiceNow’s data lake and Context Engine, taking them from early release to customer adoption. The Context Engine connects enterprise data, knowledge graphs, and AI workloads in a governed context layer for AI agents and applications. The role leads the design and implementation of lakehouse table services and context-graph ingestion, translating product requirements into scalable, observable systems with clear failure-recovery patterns. It also shapes how large language models and enterprise ontologies work together, including ontology-grounded retrieval and reasoning, and LLM-assisted ontology construction, mapping, and evolution. These capabilities should be production-grade, observable, and governed. The engineer will drive products from early release to meaningful adoption by learning from customer usage and building feedback loops. They will promote AI-native engineering practices, lead architecture reviews, raise the technical bar, represent the platform in technical and leadership forums, and coordinate with Product, Design, and partner engineering teams to align dependencies and deliver coherent experiences. The role reports to the VP of Engineering.

Requirements

10+ years of software engineering experience, including shipping complex enterprise-grade data or SaaS products at scale.
Hands-on experience building products from zero, with ownership of code and architecture decisions; experience must be beyond prototypes.
Deep cloud-native architecture experience, including multi-tenant design, large-scale REST/gRPC APIs, Kubernetes, observability, and multi-cloud platforms.
Expertise in distributed systems and event-driven architecture, focused on reliability, fault tolerance, and operational observability.
Strong data modeling across relational, graph, columnar, and streaming systems, including open table formats such as Iceberg and query engines such as Trino.
Hands-on experience with Spark, Kafka, Iceberg, and Polaris for data processing, event streaming, lakehouse table management, and catalog services.
Production-grade Python skills and hands-on experience building services with FastAPI.
Hands-on experience building LangChain or LangGraph applications and MCP servers that expose platform capabilities to AI agents.
Hands-on experience running LLMs in production, including prompt and context design, structured outputs, model evaluation, and cost and latency trade-offs.
Working knowledge of ontologies and semantic modeling, such as OWL, RDF, or SKOS, including how they constrain and ground LLM outputs.
Proficiency across the full engineering lifecycle and daily use of AI to improve delivery velocity and software quality.
Technical influence without formal authority, with a record of driving architecture decisions across teams through clear reasoning.

Nice To Haves

Knowledge graph or context graph experience, including entity resolution and graph query optimization.
Experience building ontologies or semantic layers at enterprise scale, including ontology alignment and schema mapping across data sources.
Experience fine-tuning or evaluating LLMs for entity extraction, classification, or relation extraction.
Data governance, lineage, or catalog product experience, including auditability and access control.
Production experience with data pipeline ingestion from SaaS sources.
Familiarity with OpenAPI and other AI interoperability standards.
Advanced degree in Computer Science, AI, or a related field.

Responsibilities

Own technical architecture for core components across the data lake and Context Engine, taking them from early release to customer adoption.
Lead the design and implementation of lakehouse table services and context-graph ingestion.
Translate product requirements into scalable, observable plans with clear failure-recovery patterns.
Design production-grade, observable, and governed ways for LLMs and enterprise ontologies to work together, including ontology-grounded retrieval and reasoning and LLM-assisted ontology construction, mapping, and evolution.
Drive the product journey from early release to customer adoption by learning from actual customer usage and building feedback loops.
Set the bar for AI-native engineering practices, including AI-assisted code review and test generation, and platform features that treat AI agents as first-class users.
Run in-depth architecture reviews, raise the team's technical bar, and represent the platform in technical and leadership forums.
Coordinate with Product, Design, and partner engineering teams to align dependencies and ship coherent experiences.
Drive architecture decisions across teams through clear reasoning and technical influence.