Enterprise Platform Architect

Evolver • Palo Alto, CA

8d • Hybrid

About The Position

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.

Requirements

• 10+ years of experience 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.

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.

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior