Crystalintelligence
VP of Engineering
Job Description
[AI-summarized by JobStash]
You will own the end-to-end migration from a legacy stack to an AI-native data pipeline and ensure production readiness without disrupting customer SLAs. You will drive architectural decisions, sequence service cutovers, and validate parallel runs. You will restore platform performance by reducing latency and database load, fixing stability regressions, and increasing release cadence. You will establish clear accountability across squad leads and managers, make hiring and performance decisions, and improve engineering management practices. You will build shared AI-assisted engineering infrastructure including code generation, automated testing, and agent-based migration tooling. You will translate engineering investments into measurable customer outcomes, manage the engineering budget, and communicate risks and progress to executives.
Requirements
- ā10+ years engineering experience with 5+ years leading platform data or infrastructure organizations as VP Engineering Head of Engineering or equivalent
- āLed at least one major platform migration or large-scale rebuild while maintaining continuous customer service
- āOperated low-latency high-availability distributed systems with multi-tenant SaaS workloads at production scale
- āProduction experience integrating AI into engineering workflows including agent-assisted development and AI-driven automation
- āStrong product partnership instincts
- āTrack record of building accountable high-ownership engineering organizations
- āDirect experience in one or more domains: blockchain crypto fintech payments fraud risk platforms regulatory technology or large-scale data platforms
Responsibilities
- āOwn the platform migration end-to-end
- āIntegrate the new data pipeline into all products
- āSequence migrations to preserve revenue and customer SLAs
- āDrive architectural decisions and trade-offs for cutover
- āAlign engineering product and customer success on a single migration roadmap
- āRestore API and core platform latency to target
- āReduce database load and fix stability regressions
- āIncrease release velocity to multiple deployments per week
- āLead multi-chain platform integrations across 100+ chains
- āEstablish accountability across squad leads and engineering managers
- āMake hiring performance and structural decisions for the engineering organization
- āBuild shared AI-assisted engineering infrastructure
- āDeploy organization-wide AI productivity tools
- āReduce operational expense through automation and architectural improvements
- āTranslate engineering investments into customer outcomes and revenue
- āOwn the engineering budget and vendor decisions