Enterprise Platform Architect

Evolver; Palo Alto (CA); Hybride; USD 190 000 - 270 000; Plein temps

Avantages offerts par ce poste

Competitive compensation

Flexible hybrid work

Career development

Innovative AI culture

Résumé du poste

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.

Responsabilités

• 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.

Connaissances

Distributed systems

Reliability & scalability

Cloud architecture

Azure cloud

APIs & networking

Containers

Observability

Load testing

Capacity planning

Disaster recovery

Tradeoff decisions

Leadership

Formation

Bachelor's degree

Master's degree

Outils

Azure

Databases

APIs

Description du poste

We are transforming core enterprise functions by combining frontierAI, 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, reliability, 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.
• 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.

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.

Technical Skills:

• Deep expertise in distributed systems, reliability, scalability, performance, and cloud architecture.
• Strong experience with Azure or other hyperscale cloud platforms.
• Strong understanding of databases, APIs, networking, storage, containers, and distributed compute.
• Familiarity with AI/LLM infrastructure, model APIs, inference architectures, and AI economics.
• Experience with observability, load testing, capacity planning, resilience engineering, and disaster recovery.
• Ability to make sound architectural tradeoffs across reliability, performance, complexity, and cost.
• Ability to lead architecture and drive execution across multiple engineering teams.
• Competitive Compensation: Tailored to your experience and skill set.
• Flexible Work Arrangements: Hybrid working model for work-life balance.
• Career Growth: Opportunities for professional development and leadership roles.
• Innovative Culture: Work on transformative technologies and make an impact in the AI space.

This role is hybrid in person in Palo Alto, CA.

As set forth in Evolver’s Equal Employment Opportunity policy,we do not discriminate on the basis of any protected group status under any applicable law.