Jobs at JazzX AI

Generate qualified top-of-funnel pipeline for JazzX AI’s mortgage lending solutions. Responsibilities include researching and profiling mortgage institutions, mapping decision-makers, monitoring account triggers, conducting personalized multichannel outreach, creating thought leadership content, supporting conference campaigns, re-engaging prospects, qualifying discovery conversations, handing opportunities to account executives, and feeding market intelligence to internal teams.

Leads the enterprise QA strategy for AI-powered products, including LLM evaluations, agent workflows, APIs, microservices, cloud services, automation, performance testing, release sign-off, and reliability monitoring. Manages and mentors QA teams, builds automated testing frameworks and CI/CD integrations, validates model quality and safety, drives root-cause analysis, and communicates quality risks and release readiness to leadership.

Build and lead end-to-end AI-powered applications, including React and TypeScript frontends, scalable backend services, AI agent pipelines, data and search systems, and cloud infrastructure. Drive architecture decisions, develop microservices and event-driven systems, implement observability and security practices, mentor engineers, and promote AI-native development. The role requires hands-on full-stack ownership from prototyping through production, with expertise in LLM integrations, cloud-native platforms, databases, automated testing, and DevOps.

Lead architecture and implementation of JazzX AI’s cloud-native enterprise AI platform. Own distributed systems, service orchestration, APIs, infrastructure foundations, reliability, observability, security, and developer experience. Drive architectural reviews, technical strategy, production readiness, SLAs, SLOs, and cross-team execution while mentoring senior engineers. The role requires hands-on software engineering across the full SDLC and experience with containers, cloud platforms, infrastructure as code, event-driven systems, databases, workflows, and AI/ML infrastructure.

Lead the architecture, development, and production deployment of reinforcement learning systems for enterprise AI. Design scalable training pipelines, simulation environments, multi-agent and hierarchical RL frameworks, model-serving infrastructure, evaluation systems, and safety controls. Apply advanced RL and LLM techniques, optimize distributed training, collaborate with product and platform teams, establish governance standards, and mentor engineers and researchers while bridging AI research with reliable enterprise production systems.

Owns U.S. residential mortgage underwriting policy for an AI-powered mortgage application. Interprets Fannie Mae, Freddie Mac, FHA, VA, and USDA guidelines; validates AI-generated underwriting decisions; translates policies into business rules and decision logic; supports LOS workflows and product requirements; identifies policy gaps; and trains cross-functional teams and clients on underwriting standards.

Lead product strategy and execution for AI-native, enterprise workflow and platform products. Define high-impact problems, design reusable platform capabilities, partner with engineering and customers, and measure outcomes to drive adoption and continuous improvement.

Owns end-to-end product design for complex enterprise and AI-powered workflows. Conducts user research, develops and validates concepts, creates prototypes, designs intuitive interactions, maintains design systems, and partners with Product and Engineering to deliver production-ready experiences. The role requires simplifying complex processes, designing human-AI interactions, using AI tools to accelerate workflows, and influencing product direction through customer insights and data.

Lead the quality strategy for AI-powered products, including LLM outputs, agent workflows, APIs, cloud services, and enterprise deployments. Manage and mentor QA teams, build automation frameworks, develop AI-specific evaluation and regression testing, oversee performance and API testing, integrate tests into CI/CD, manage release sign-offs, and monitor model reliability and production behavior. Partner with Product, ML, Platform, DevOps, and leadership teams to drive quality initiatives.

Leads customer-facing technical delivery as a hands-on software engineering owner. Designs and implements production-grade backend services, APIs, distributed systems, integrations, and GenAI applications involving LLMs, RAG, and agentic workflows. Owns architecture, reliability, observability, troubleshooting, customer rollouts, and technical escalations. Mentors engineers, establishes delivery standards, collaborates across product and research teams, and converts customer insights into reusable platform components and patterns.

Own end-to-end product design for AI-powered enterprise software, from customer discovery and problem definition through prototyping, high-fidelity design, launch, and iteration. Partner with Product and Engineering to simplify complex workflows, create accessible and trustworthy experiences, evolve the design system, and raise UX and visual quality. Staff-level designers are expected to operate autonomously, influence product direction, and lead ambiguous, high-impact initiatives.

Lead the end-to-end technical product lifecycle for an AI-powered commercial lending suite. Define requirements, workflows, AI behavior, data models, APIs, integrations, evaluation standards, release readiness, monitoring, and deployment playbooks. Partner with Engineering, AI/ML, UX, Customer Success, and enterprise stakeholders to deliver reliable, explainable, reusable products for regulated environments while balancing customer needs with platform scalability.

Contract SME to design end-to-end commercial lending workflows, produce reusable artifacts (workflow maps, persona maps, document/data matrices, credit-package models, policy/approval logic, playbooks, validation rubrics), assess product readiness, and advise on where AI can safely support lending and servicing processes.

The Senior Staff AI Engineer will lead the design and optimization of reinforcement learning systems for enterprise applications, focusing on scalable architectures and production deployment. Responsibilities include mentoring, collaboration, and ensuring compliance with AI ethics and safety standards.

Build and operate core backend services, APIs, and platform components for enterprise AI; integrate LLMs, retrieval and agent workflows; ensure reliability, observability, testing, and scalability; collaborate with research, product, and design to productionize AI capabilities.