SVP, Engineering

About the role

The SVP, Engineering is responsible for the platform and application/solution engineering organization, AI systems quality, reliability, and the engineering function that turns the JazzX platform into an enterprise-grade, scalable product. This is a full-stack engineering leadership role with ownership across platform architecture, AI systems, application and solution engineering, infrastructure, security, and organizational scaling.

Scope

End-to-end JazzX engineering function across the shared platform and domain application/solution layers, enabling reuse at scale while delivering measurable customer outcomes in production. Includes architecture, development, delivery, reliability, AI quality, security, infrastructure, operations and engineering org design and scaling.

Team

• Full-stack engineering organization across platform engineering, application and solution engineering, AI/ML, data and integrations, infrastructure, security, DevOps, and QA.
• Geography: Distributed engineering organization across the US and India, with senior leadership in both.
• GTM organization is currently in the US, expected to grow globally.

What You Will Own

• Platform Engineering & AI Systems: Platform strategy and architectural evolution. Own the engineering strategy across the JazzX platform and the solutions built on top of it. Define the next stage of architectural maturity—setting priorities across platform capabilities, domain applications, and new deployment models so JazzX scales as a platform company, not a bespoke services business. Aligns engineering strategy tightly with business growth goals building toward a platform capable of supporting a $1B+ ARR trajectory. Evolve JazzX's AI-native capabilities from functional to enterprise-grade and production-hardened. Build evaluation frameworks, benchmarks, regression coverage, and feedback loops. Strengthen explainability, auditability, governance, and human-in-the-loop controls based on real deployment needs. Establish consistent standards for reliability, observability, and failure handling as usage scales across customers and domains. Architecture boundary judgment and reuse discipline. Make clear, durable decisions about what belongs in the shared platform versus solution layers, configuration/integration, or customer-specific scope. Systematically convert repeated patterns from live deployments into reusable services, frameworks, and tooling. Protect the platform from fragmentation while enabling speed.
• Delivery, Scale & Market Expansion: Engineering operating system for scale. Evolve current execution practices into a disciplined, high-throughput engineering system. Strengthen planning, design review, and delivery rhythms. Raise the bar on release quality, incident response, and observability. Make trade-offs explicit and data-driven across scope, timelines, reliability, and technical debt. Deployment velocity and domain expansion. Turn current deployment experience into a repeatable, faster go-to-production engine. Reduce time-to-value for new customers and domains. Capture and productize deployment learnings into platform improvements that compound across every new vertical. The target: a new domain in production in roughly three months, with minimal architecture changes required. Platform surfaces for partners and ecosystem. Evolve the JazzX platform from internally usable to externally extensible and partner-ready. Mature APIs, integration patterns, configuration models, documentation, and developer experience so partners, portfolio teams, and advanced customers can extend the platform with decreasing reliance on core engineering.

What We Are Looking For

• The strongest candidates will combine scaled AI-native engineering leadership, strong architectural judgment, production operations experience, and the ability to build both a platform and the organization required to scale it.
• Enterprise Platform & Vertical Solution Leadership: Has built or scaled engineering organizations for AI-native enterprise software—owning both a shared platform and the application/solution layers on top. Navigates the inherent tension between platform investment, product development, and customer delivery without letting any dimension stall. Transitions teams from early-stage experimentation and speed to disciplined, repeatable systems for production at scale. Aligns engineering execution tightly with business growth goals (toward a $1B+ ARR trajectory).
• Experience in 0–100 Fast-Growth Startups and High-Growth Scaled Businesses: Has experience scaling a live engineering organization from early-stage to production at scale—while also launching new capabilities, entering adjacent markets, or expanding into new domains. Comfortable operating in both modes at the same time.
• AI-Native and Technically Credible: Deep fluency in modern AI system design and real-world deployment challenges—including LLMs, retrieval systems, agents, orchestration, evaluation frameworks, and prompt/context engineering. Understands probabilistic system behavior, guardrails, feedback loops, and quality measurement. Builds AI systems that power mission-critical workflows despite imperfect models and evolving capabilities. Bridges AI components with distributed systems, APIs, and enterprise-grade reliability expectations. Credible partner to Product and able to make strong engineering judgments in a fast-moving AI environment.
• Strong Engineering Operator: Translates strategy into clear architecture, delivery rhythms, and execution. Owns roadmap execution from design to stable production systems. Establishes strong SDLC practices, observability, release discipline, and operational rigor without creating unnecessary processes. Platform, Infrastructure & Cost Discipline: Designs platforms serving multiple constituencies: internal teams, enterprise customers, and partners. Understands cloud and AI infrastructure, inference cost dynamics, and scaling trade-offs. Makes principled decisions across performance, cost, and AI quality. Builds instrumentation and operating rhythms to continuously manage cost and efficiency at scale.
• Enterprise-Grade Systems, Governance & Regulated Domain Experience: Deep understanding of enterprise identity, security, permissions, auditability, data lineage, and policy enforcement. Designs for reliability, recoverability, and compliance from the ground up. Comfortable operating in regulated domains where correctness, controls, and process fidelity are critical—including lending, insurance, healthcare, and legal. Treats trust, safety, and governance as first-class engineering requirements, not compliance afterthoughts.
• Global Leadership Capability: Effective leading distributed engineering teams across the US and India. Drives alignment, execution quality, and talent development across geographies and time zones.
• Builder of Teams and Culture: Has recruited and developed strong engineering leaders and senior engineers. Establishes clear decision rights, ownership, and operating cadence. Builds an engineering culture with urgency, craftsmanship, accountability, and company-first thinking—without slowing innovation.

Success Looks Like

• Building a world-class AI-native engineering organization.
• Harden JazzX into an enterprise-grade platform ready for hypergrowth.
• Enabling the company to scale repeatably across domains, customers, and partners.