Senior Platform Engineer

Role overview

Platform engineering

• Design and build shared platform services, APIs, SDKs, and engineering tools.
• Develop orchestration for applications involving services, tools, models, and workflows; create consistent interfaces for shared capabilities.
• Integrate AI models and supporting services into reusable capabilities.
• Build identity-aware services with authentication and authorization enforced at the appropriate layer.
• Create templates, libraries, examples, and automation for application development; support interactive and programmatic consumers.
• Set standards for API design, service contracts, versioning, testing, and observability; build tracing, monitoring, and operational controls.
• Partner with infrastructure and application teams to ensure scalability, reliability, and security.
• Turn recurring engineering problems into reusable solutions, improve self-service, review code, document decisions, and establish standards.

Experience and technical requirements

• 8+ years in software, backend, platform, or infrastructure engineering.
• Production experience building and operating internal platforms, shared services, or developer tooling.
• Strong hands-on experience with AI-enabled applications, agent systems, or comparable technologies.
• Strong Python and Go skills; Java or Node.js experience is advantageous.
• Experience with APIs and SDKs, workflow orchestration, tool integration, asynchronous processing, or multi-step service interactions.
• Strong understanding of distributed systems and service architecture.
• Experience with authentication, authorization, OIDC/SSO, and identity-aware services.
• Experience with Docker, Kubernetes, modern CI/CD, and AWS or another major cloud platform.
• Experience with infrastructure as code such as Terraform or Crossplane.
• Strong understanding of reliability, security, scalability, and operational trade-offs; focus on developer experience and adoption.

AI and application platforms

• Relevant experience may include agent orchestration, workflow systems, AI tool calling, and integration with business applications.
• Other relevant areas include model gateways, provider integrations, retrieval and data access, evaluation, tracing, guardrails, and standard interfaces.
• LangGraph, LangChain, LlamaIndex, Google ADK, or comparable frameworks are listed as examples; production AI or agent-system experience is highly desirable.