Staff Software Engineer, Conversational and Agentic AI Platform Services

ServiceNow•Santa Clara, CA

4d•$166,500 - $291,400•Hybrid

About The Position

ServiceNow is looking for a strong software engineer and technical lead with deep software design and architecture skills, strong coding ability, and the technical judgment to build reliable production systems at enterprise scale. ServiceNow's core platform is built on a powerful and highly scaled Java service that supports some of the world's largest enterprise workflows. In this team, we are extending that platform with modern Python and Java microservices that complement the main platform architecture and enable new AI-native capabilities. These services are designed to run in Kubernetes-based environments while integrating deeply with the broader ServiceNow platform. We are focused on production-grade services that bring together enterprise data, AI model integrations, scalable APIs, evaluation workflows, and reliable deployment patterns. You will own the design, development, and operation of containerized backend services running in Kubernetes environments. You will apply strong distributed systems and production reliability judgment to build scalable, maintainable services, and you will lead technical design across ambiguous, high-impact problem spaces. Experience working on AI, GenAI, ML-powered, or data-intensive products will help you be especially successful in this role.

Requirements

8+ years of professional software engineering experience, with strong fundamentals in data structures, algorithms, distributed systems, APIs, and backend service design
Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving (including using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI's potential impact)
Demonstrated experience as a technical lead, Staff Engineer, or equivalent senior individual contributor responsible for architecture, technical direction, code quality, and mentoring
Strong backend engineering skills in Java, Python, or equivalent with hands-on experience designing, building, and operating production services
Hands-on experience building or operating containerized applications deployed in Kubernetes, OpenShift, or similar orchestration environments
Experience designing scalable APIs, asynchronous processing systems, queues, event-driven services, or data-processing pipelines
Familiarity with cloud-native infrastructure, service observability, logging, monitoring, reliability engineering, and production troubleshooting
Ability to make pragmatic architecture decisions across performance, reliability, security, maintainability, and delivery speed
Ability to lead other engineers through code reviews, design reviews, and technical mentorship
Strong communication skills and the ability to partner across product, research, engineering, infrastructure, and security

Nice To Haves

Prior experience working on AI/ML products, collaborating with research teams, or translating advanced AI/ML capabilities into production software
Knowledge of NLP, search, and knowledge extraction
Experience with multi-modal systems such as document ingestion and processing, image processing, and voice
Working experience with frontier AI SDKs such as Anthropic, Google, or OpenAI
Familiarity with prompt engineering, structured outputs, tool calling, agentic design patterns, Model Context Protocol, or AI-assisted development workflows
Experience with observability tools such as Prometheus, Grafana, Instana, or equivalent monitoring/logging platforms
Knowledge of MLOps or applying machine learning models to production use cases
Experience with infrastructure backends such as PostgreSQL, Redis, Kafka, RabbitMQ, S3-compatible object storage, or equivalent technologies
Published work, patents, conference papers, or open-source contributions related to AI systems, knowledge systems, search, retrieval, or large-scale data processing

Responsibilities

Designing, architecting, and implementing Java and Python microservices deployed on Kubernetes
Designing APIs, service boundaries, data models, and integration patterns for AI-enabled products
Building reliable distributed systems that handle concurrency, queueing, retries, fairness, backpressure, and failure recovery
Integrating frontier AI SDKs such as Anthropic, Google, and OpenAI into production software systems
Applying prompt engineering, structured outputs, model evaluation, and production observability to GenAI use cases
Partnering with research, product, security, and infrastructure teams to ship enterprise-grade services
Raising the engineering bar through architecture reviews, code reviews, mentoring, and technical direction