Enterprise Platform Architect
Drive availability, scalability, performance, and cost efficiency of Evolver's enterprise AI SaaS platform, working across engineering, cloud infrastructure, DevOps, QA, and AI teams with deep focus on distributed systems, Azure cloud architecture, and AI/LLM infrastructure economics.
- Compensation
- Not disclosed
- Location
- Palo Alto, CA
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 10+ years
- Category
- Architecture
- Posted
Skills
Must have
- 10+ years architecting and building enterprise SaaS platforms or similarly complex production systems
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- 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
Full description
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.
ResponsibilitiesAvailability & 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.