Principal Machine Learning Engineer

Join ServiceNow as a Principal Machine Learning Engineer, where you will lead the technical direction and architecture of cloud-native platform services supporting Agentic AI initiatives. You will drive the design and implementation of scalable Kubernetes-based infrastructure, partner with various teams to modernize critical services, and establish architectural standards and engineering best practices. Additionally, you will mentor senior engineers, lead architecture reviews, and work closely with teams developing AI-powered experiences.

- Lead the technical direction, architecture, and evolution of cloud-native platform services supporting Agentic AI initiatives. - Drive the design and implementation of scalable Kubernetes-based infrastructure that supports AI/ML and conversational workloads at enterprise scale. - Establish architectural standards, engineering best practices, and platform patterns that can be leveraged across multiple product teams.

- Strong understanding of distributed systems principles, including resiliency, fault tolerance, scalability, performance optimization, and observability - Experience building platform services used across multiple engineering teams and products - Experience leading technical strategy and influencing architecture across multiple organizations without direct authority - Proven experience designing, building, and operating highly scalable cloud-native architectures - Experience with AWS, Azure, or Google Cloud at large scale - Expert knowledge of Kubernetes, containers, orchestration platforms, and modern cloud infrastructure - Deep expertise in Java, Go, Python, or similar systems programming languages - Strong software engineering fundamentals including object-oriented design, system design, architecture patterns, and performance optimization - Demonstrated track record building new platform capabilities from concept through large-scale production adoption - Expertise in CI/CD, infrastructure automation, GitOps, Terraform, Helm, ArgoCD, or comparable technologies - Strong communication skills with the ability to explain technical tradeoffs to engineering and executive leadership - 15+ years of experience building large-scale distributed systems, cloud platforms, infrastructure services, or enterprise SaaS products - Experience building infrastructure for AI/ML, generative AI, agentic systems, or conversational platforms - Experience supporting GPU workloads, model serving, inference platforms, or AI runtime environments - Experience with internal developer platforms, platform engineering, SRE, or cloud infrastructure organizations - Knowledge of multi-agent architectures, orchestration platforms, workflow engines, or large-scale automation systems - Experience with modern observability platforms including OpenTelemetry, Prometheus, Grafana, Datadog, or equivalent technologies - Experience modernizing legacy architectures into cloud-native distributed services