Enterprise Platform Architect

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.

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.