> Markdown version of [/jobs/ext/4605445-platform-engineer](https://www.wearedevelopers.com/jobs/ext/4605445-platform-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Platform Engineer - **Company:** NxT Level - **Location:** New York, NY, United States - **Experience:** Experienced - **Salary:** $200,000.0 - **Contract:** Permanent contract - **Skills:** Admin Tools, Application Programming Interfaces (APIs), Artificial Intelligence, Systems Engineering, Software Quality, Continuous Integration, Data Infrastructure, Programming Tools, Distributed Systems, OAuth, Service-Oriented Architecture, TypeScript, Datadog, Delivery Pipeline, Large Language Models, Backend, Agentic-AI, Kubernetes, Api Gateway, Dynatrace - **Published:** November 6, 2026 - **Apply:** https://www.adzuna.com/details/5777328638 ## About the Role * 3+ years of experience in platform, infrastructure, backend, or systems engineering * Strong experience with service-oriented architecture, networking, and distributed systems * Ability to reason clearly about how data, traffic, and failures move through complex systems * Strong TypeScript experience * Hands-on experience operating production services on Kubernetes * Experience taking ambiguous technical goals from prototype to production * Strong code quality and data modeling skills * Product-minded approach to platform engineering * Ability to build tooling that improves the day-to-day experience of engineers * Comfort using AI aggressively in personal engineering workflows * High ownership, strong communication, and a bias toward building Bonus Experience * Experience with MCP, OAuth, API gateways, or multi-tenant integration platforms * Experience with OTEL, distributed tracing, Datadog, observability, or SRE best practices * Experience building internal admin tools or developer tooling * Experience building systems used by both humans and AI agents * Experience with LLM systems, agent tooling, context management, tool use, or sandboxing Who Thrives Here The right person is a builder who enjoys owning foundational systems in a fast-paced startup environment. They think in systems, care deeply about reliability and developer experience, and enjoy creating leverage for every engineer around them. This person is excited to work in person with an ambitious, thoughtful team in New York and is energized by zero-to-one product development, rapid iteration, and AI-native engineering., The ideal candidate is a platform-minded engineer who loves building infrastructure that other engineers depend on. They have strong backend and systems fundamentals, care about reliability and developer experience, and are excited to build the connective tissue between AI agents, customer environments, and production infrastructure. ## Description Our client is hiring a Platform Engineer to own the foundational systems that power product engineering velocity, service infrastructure, and the connectivity layer between AI agents and customer environments. This role is core to how the company ships. You'll build and operate the systems behind every agent invocation, every customer onboarding workflow, and every internal engineering process. This includes internal admin tooling, deployment and observability pipelines, integration infrastructure, MCP gateway systems, and the platform primitives that help customers get value in days instead of weeks. You'll partner closely with product, field engineering, and research teams. Your work will compound across every team building on top of the platform. What You'll Do * Build platform systems that accelerate engineering velocity across the company * Own internal admin tooling, CI/CD, deployment pipelines, dev/demo environments, and AI-native engineering workflows * Build and maintain core service infrastructure across Kubernetes, observability, shared libraries, secret management, and deployment abstractions * Set the standard for what "production-ready" means across the engineering organization * Own data infrastructure that supports evaluations, experimentation, agent behavior, customer telemetry, resource graphs, and investigation traces * Design clean schemas, storage patterns, and access layers that keep critical product data reliable and usable * Build and operate the connectivity layer between agents and customer environments * Own MCP gateway systems, OAuth, credential management, customer integrations, API routing, and self-hosted server lifecycle * Improve reliability, security, and enterprise trust across multi-tenant integration systems * Create platform tooling that is adopted and loved by both engineers and AI agents