Enterprise Platform Architect: AI-Driven & Scalable

Evolver

Palo Alto (CA)

Hybrid

USD 190,000 - 270,000

Full time

14 days+

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Benefits offered by this job

Competitive compensation

Flexible hybrid work

Career development

Innovative AI culture

Job summary

Evolver is building enterprise AI platforms in Palo Alto, combining frontierAI engineering with domain expertise to deliver reliable, scalable, and cost-efficient solutions for Fortune 1000 customers.

The Enterprise Platform Architect will work across Engineering, Cloud Infrastructure, DevOps, QA, and AI teams to define measurable requirements, identify bottlenecks, and drive platform enhancements for availability, resilience, scalability, and performance.

Qualifications

Experience: 10+ years of experience architecting and building enterprise SaaS platforms or similarly complex production systems.
Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.

Responsibilities

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.
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.
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.
Lead architecture and drive execution across multiple engineering teams.