Staff Software Engineer- AI Platform

Role overview Design, build, and operate shared platform infrastructure that lets applications and AI agents access foundation models securely and reliably. This staff-level engineering role spans model gateways and provider integrations, routing and fallback, usage controls, observability, reliability, and governance. The work emphasizes reusable, self-service capabilities and the full lifecycle of production systems, including architecture, security, cost, and developer experience.

Responsibilities - Architect and deliver scalable platform services for model access, cloud AI services, and provider integrations. - Build routing, fallback, usage management, and governance capabilities that support a growing set of AI applications. - Establish operational practices for reliability, observability, failure handling, and production readiness. - Partner with application engineers, product, security, and infrastructure teams to shape platform direction and delivery. - Set engineering standards, improve code architecture and quality, and make pragmatic decisions across competing requirements. - Mentor engineers and help teams build maintainable, secure, and observable systems.

Requirements - Bachelor’s degree or higher in computer science, engineering, or a related field. - At least eight years of production engineering experience building highly scalable systems. - At least four years as a trusted technical decision-maker, balancing near-term needs with longer-term value. - At least four years querying large, multi-table datasets with SQL or similar tools. - Experience architecting, developing, and deploying large distributed systems, as well as working with cloud services and CI/CD pipelines. - Strong familiarity with server-side technologies such as Java, Python, Scala, C#, C++, or Go.

Nice to have - Experience with production platforms, distributed systems, developer platforms, or high-availability shared services. - Familiarity with LLM infrastructure, model gateways, foundation model APIs, or managed AI services. - Experience with cloud infrastructure, Kubernetes, containers, and infrastructure as code. - Knowledge of observability practices such as tracing, metrics, logging, alerting, service objectives, and dashboards. - Background in APIs and self-service platform design, identity and access controls, secrets, security governance, cost management, or regulated data systems. - Ability to assess tradeoffs among reliability, performance, security, cost, scale, and developer experience.