Enterprise Platform Architect

About Us We are transforming core enterprise functions by combining frontier AI, strong engineering, and deep domain expertise. Our platform is used by a growing number of Fortune 1000 companies to execute complex, high-value work. We are building enterprise AI for environments where accuracy, reliability, security, scalability, and economics matter. Role Overview We are looking for an experienced Enterprise Platform Architect to further push the availability, reliably, scalability, performance, and cost efficiency of our platform. You will work across Engineering, Cloud Infrastructure, DevOps, QA, and AI teams to establish measurable requirements, identify architectural platform bottlenecks and opportunities, and define enhancements. Responsibilities Availability & Resilience Define measurable availability, resilience, and recovery requirements. Architect for failures across infrastructure, APIs, databases, external dependencies, and AI models. Design redundancy, failover, retries, timeouts, graceful degradation, and recovery mechanisms. Identify and eliminate critical single points of failure. Establish resilience, failover, and recovery testing with clear production-readiness metrics. Identify gaps, drive remediation, and validate readiness for enterprise production. Scalability & Performance Define measurable targets for throughput, concurrency, latency, document size, storage growth, and model capacity. Architect the platform to scale predictably across customers, workloads, and data volumes. Lead capacity planning across compute, storage, databases, networking, and AI infrastructure. Establish load, stress, endurance, and performance-testing standards. Identify and eliminate architectural and performance bottlenecks. Maintain performance benchmarks and ensure the platform meets enterprise-scale requirements before production. Cost Efficiency Define and track platform unit economics, including cost per transaction, workflow, and AI execution. Establish cost targets and identify the primary drivers of platform economics. Optimize model selection, routing, caching, batching, and reuse. Move workloads from expensive LLM reasoning to code, ML, smaller models, or deterministic systems where appropriate. Improve infrastructure utilization and eliminate unnecessary computation. Ensure the platform remains economically viable as workload volume and complexity scale. </sp