David Lin

As a Principal AI Engineer, I architect and execute the core intelligence behind SprintiQ AI's agile project management platform. My focus is on designing, building, and deploying the scalable AI systems that power our product, from advanced LLMs and predictive analytics to process automation.

My core responsibilities and expertise include:

- LLM System Architecture & Implementation: Building and refining our platform's AI brain using RAG for knowledge retrieval, fine-tuning open-source models for domain-specific tasks, and orchestrating complex workflows with LangChain.

- End-to-End AI Integration: Seamlessly embedding intelligent features—like automatic sprint planning, predictive risk detection, and generative retrospectives—into a secure, full-stack application. My work spans from the Python backend (FastAPI) to the user-facing frontend (Next.js).

- Scalable, Cloud-Native AI Systems: Architecting and developing our platform as a secure, multi-tenant SaaS solution on AWS/GCP. This includes designing robust MLOps pipelines, ensuring data privacy compliance (SOC 2, GDPR), and implementing efficient CI/CD (GitHub Actions, ArgoCD) for rapid, reliable iteration.

- Data-Driven Product Intelligence: Leveraging predictive analytics to transform project data into actionable insights, enabling features that proactively assist teams and streamline the entire software development lifecycle.

My engineering philosophy centers on building AI that is not only innovative but also reliable, scalable, and directly aligned with user needs. I am deeply involved in the entire lifecycle, from initial data processing and model training to the final deployment of a production-ready, intelligent application.

My focus is on building and integrating sophisticated Al capabilities directly into client ecosystems.

• • Custom Al Application Development: Directed the full lifecycle of multiple Al applications, from initisi cata pipeline design to the deployment of fine-tuned models and intuitive user interfaces, solving specific client use cases

• •Al & Real-Lime Streaming Integralion: Engineered hybrid solutions that combine real-time data streaming architectures with Al models to power dynamic, intelligent features within client platforms, enabling live analytics and proactive insights.

• • Cross-Functional Technical Leadership: Managed a distributed team of developers and Al engineers to deliver robust, enterprise-grade software, ensuring solutions adhere to principles of quality, scalability, and creative problem-solving.

• • End-to-End Solution Delivery: Parinered with clients to translate business needs into technical reality, leveraging modem Al stacks and cloud infrastructure to build efficient, customized lutions that align with their strategic plans.

As a Video Streaming Engineer st Disney Streaming. I design and optimize large content delivery systems that ensure seamless playback for millions of viewers across Disney+, Hulu, and ESPN+. My work focuses on adaptive bitrate streaming (ABR), CDN optimization, and latency reduction to deliver high-quality video across devices and netvorks. I colaborate with encoding teams to improve video compression (H.265/AV1) and Implement QoE (Quality of Experience) metrics to monitor buffering, startup limes, and playback reliability. Using real-time analytics, I troubleshoo! performance bottlenecks and enhance mulli-CON strategies for global scalability. Additionally, I contribute to DASH/HLS packaging and DRM Widevine. FarPlay, PlayReady! integrations to secure premium content. My goal is to innovate streaming tech that keeps audiences engaged with ultra- low-latency live evenis and K/HDR on-demand experiences.