Next Step Systems - National IT Recruiting Firm

• Own the application engineering strategy and execution across backend, mobile, and desktop. • Lead and grow a small, senior applications team, including backend, mobile, and desktop engineers. • Set the architectural direction for Artificial Intelligence (AI)-powered product workflows, APIs, and client integrations. • Ensure AI capabilities are integrated into the product with clear abstractions, predictable behavior, and graceful failure modes. • Partner closely with Machine Learning leadership to translate model capability into shippable product features. • Make high-impact decisions across latency, cost, reliability, security, and user experience. • Establish clear ownership boundaries across backend, mobile, and desktop to prevent architectural drift. • Ensure production readiness: observability, monitoring, retries, fallbacks, privacy, and cost controls. • Balance speed and discipline shipping quickly without compromising long-term system quality.

• Set and evolve the research direction for the core intelligence, including context representation, memory, reasoning, planning, and orchestration. • Decide when to design new model architectures versus adapting or leveraging frontier open-source or commercial models. • Define evaluation frameworks that measure real-world usefulness, robustness, safety, and long-term behavior, not benchmark vanity. • Own alignment, safety, and guardrail strategy as first-class product concerns. • Guide exploration of frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal systems. • Shape early product intelligence direction in close partnership with product and application engineering. • Set the technical bar for research rigor, judgment, and taste across the organization.

• Research and define end-to-end Artificial Intelligence (AI) system requirements from capability to behavior to user impact. • Translate model capabilities, data constraints, and evaluation results into clear product and system decisions. • Make hard trade-offs across quality, latency, cost, reliability, and UX. • Work closely with Machine Learning (ML), backend, and mobile engineers on system design, evaluation, and iteration. • Define and evolve evaluation frameworks across offline metrics, online experiments, and human feedback. • Drive execution with clear specs, strong judgment, and disciplined prioritization. • Ensure systems ship quickly, safely, and reliably, with strong feedback loops. • Own product quality end-to-end correctness, predictability, and user trust.

• Own the application engineering strategy and execution across backend, mobile, and desktop. • Lead and grow a small, senior applications team, including backend, mobile, and desktop engineers. • Ensure production-grade reliability: observability, monitoring, retries, fallbacks, and failure handling. • Set the architectural direction for AI-powered product workflows, APIs, and client integrations. • Ensure AI capabilities are integrated into the product with clear abstractions, predictable behavior, and graceful failure modes. • Partner closely with Machine Learning (ML) leadership to translate model capability into shippable product features. • Make high-impact decisions across latency, cost, reliability, security, and user experience. • Establish a strong execution culture through code reviews, design reviews, and hands-on technical leadership. • Ensure production readiness: observability, monitoring, retries, fallbacks, privacy, and cost controls. • Balance speed and discipline, shipping quickly without compromising long-term system quality.

• Own the end-to-end strategy and roadmap for all internal tools, platforms, and automation treating internal technology as a product, not a cost center. • Make strategic build-vs-buy decisions, knowing when a custom-built solution creates a lasting advantage and when a SaaS tool is the right answer. • Map current and next-state process flows across the entire internal toolchain and lead systems transformation for internal teams. • Architect and maintain the full engineering lifecycle for internal platforms from ideation and design through deployment, iteration, and deprecation. • Build seamless, API-first ecosystems that integrate internal tools across HR systems, finance platforms, knowledge management, CRM, and developer infrastructure. • Own system reliability and operational resilience: establish success metrics for uptime, performance, and employee productivity. • Design scalable, secure architectures using cloud-native principles, microservices, and modern integration patterns. • Lead the strategy for integrating AI and LLMs into internal workflows reimagining how knowledge is shared, decisions are supported, and work is executed across the organization. • Deploy intelligent automation tools including AI where appropriate to streamline internal processes and improve decision-making. • Evaluate and integrate AI-assisted troubleshooting, proactive recommendations, and intelligent automation into the internal platform layer. • Stay ahead of technology trends and drive continuous experimentation you build prototypes, not slide decks. • Reduce cognitive load for internal users by providing golden paths, standardized workflows, and self-service capabilities. • Ensure frictionless onboarding and seamless integration across the tool ecosystem. • Measure platform success through adoption rates, user satisfaction, DORA metrics, and productivity impact not feature count. • Build, lead, and mentor a high-performing team of engineers and engineering managers. • Cultivate a collaborative engineering culture rooted in ownership, speed, and craftsmanship. • Provide technical mentorship and create growth paths for individual contributors and managers alike. • Foster psychological safety and a feedback-driven environment that empowers people to do their best work. • Partner with People, Finance, Engineering, Legal, and Operations leadership to translate complex business needs into a unified technical vision. • Serve as the bridge between business stakeholders and the engineering team you speak both languages fluently. • Align internal platform investments with broader company strategy and demonstrate measurable ROI.

• Help structure engineering workstreams and ensure projects move forward efficiently. • Identify risks, unblock engineers, and maintain steady development velocity. • Ensure high engineering standards while keeping teams focused on shipping. • Translate product direction and technical strategy into clear engineering execution. • Contribute to key architecture discussions and technical decisions. • Support engineers in evaluating system design choices and implementation trade-offs. • Ensure systems are implemented in a scalable and maintainable way. • Build and scale a strong engineering team across China, US and Europe. • Recruit high-quality engineers and help maintain a strong hiring bar. • Establish structured hiring processes and interview standards. • Work closely with product, research, and leadership teams. • Ensure alignment across different engineering efforts and locations. • Help resolve technical and organizational bottlenecks.

• Define the long-term architecture for AI systems, infrastructure, and developer platform. • Evaluate trade-offs between speed of iteration and long-term system design. • Ensure systems are designed for scalability, reliability, and long-term evolution. • Guide key decisions across model integration, data pipelines, distributed systems, and product architecture. • Work with engineers to translate product direction into clear technical execution. • Help structure engineering workstreams and keep teams aligned on priorities. • Maintain high engineering standards while keeping the team focused on shipping. • Establish engineering culture, development practices, and technical standards across the company. • Build and scale a world-class engineering team across key talent hubs including China and US. • Identify strong technical leaders and help build a high-quality engineering organization. • Define hiring standards and interview processes to maintain a high engineering bar. • Work closely with product, research, and leadership teams. • Ensure technical workstreams move forward smoothly across teams and locations. • Help resolve cross-team technical and execution challenges.

• Build and ship iOS features using Swift • Create clean mobile flows for insurance, payments, claims, renewals and financial services • Work with product and design to simplify complex user journeys • Integrate backend APIs and ensure mobile flows are stable and reliable • Improve performance, crash rate, loading states, responsiveness, memory usage and battery efficiency • Use analytics, user behavior and production issues to improve the product • Build AI-assisted mobile experiences only where they genuinely improve the user journey

• Lead the design and development of production AI systems powering insurance workflow automation. • Architect AI orchestration layers connecting LLMs, backend services, and business workflows. • Own end-to-end AI system design, including inference pipelines, routing, caching, and fallback strategies. • Drive engineering decisions around latency, reliability, cost, and scalability of AI services. • Lead implementation of Observability Systems (logging, monitoring, tracing, alerting). • Review and guide backend AI implementation across engineering teams. • Collaborate with product, backend, DevOps, and operations teams to ship end-to-end AI features. • Debug complex production issues across distributed AI systems and lead root-cause analysis. • Define engineering standards and best practices for AI system development. • Mentor engineers and elevate technical execution quality across teams.

• Responsible for day-to-day ownership, optimization, and support of the company’s CRM platform. • Serve as the primary liaison between business teams and the CRM vendor. • Manage day-to-day system administration, including user support, troubleshooting, and system maintenance. • Train new and existing employees on CRM functionality, processes, and best practices. • Ensure accuracy, consistency, and completeness of CRM data through regular audits and maintenance. • Develop and maintain dashboards, reports, and queries to support business insights.