• Build, iterate, and scale AI-powered features using LLMs and modern AI platforms, with a strong focus on application-level integration • Help define and drive Prokeep’s AI strategy, including where and how AI should be used across the product • Serve as an internal evangelist and subject-matter expert for AI, helping teams understand capabilities, tradeoffs, and best practices • Lead AI evaluation and testing efforts, owning not just the AI engines but also the testing and evaluation frameworks used to measure quality, reliability, and performance • Design and maintain internal tooling to support AI development, experimentation, and evaluation • Partner with Product, Engineering, and Leadership to identify high-impact opportunities where AI can improve customer workflows • Make thoughtful decisions around model selection, orchestration, prompting strategies, and cost/performance tradeoffs • Work closely with our Data Engineer to ensure the right data foundations exist to support AI features • Ensure AI systems are production-ready, observable, and scalable • Improve systems already in flight while contributing to greenfield initiatives—AI is not a silo here

• Shape the vision for how AI/ML, particularly LLMs, integrate into Homebound’s products and operations. Define technical standards for experimentation, deployment, and evaluation. • Design and build scalable AI systems that can handle real-world production workloads and evolve with the business. • Deliver high-quality, production-grade code daily while ensuring observability, maintainability, and reliability. • Raise the bar across engineering by teaching AI best practices, reviewing designs, and guiding engineers through complex technical challenges. • Stay abreast of the rapidly evolving AI landscape, bringing forward cutting-edge ideas while focusing investment where it creates clear business impact.

• Develop the long term vision and roadmap for Artera’s AI platform that will allow the company to continue to scale in terms of both increased inference volume and development workloads. • Accountable for Artera’s ML compute infrastructure including scaling up Artera’s Foundation Model development by developing distributed training infrastructure and developer libraries. • Build and evolve the core libraries used by AI scientists to develop, launch, and monitor AI products. • Work with model developers to optimize GPU and CPU efficiency and data throughput of large-scale foundation models and downstream model training runs. • Optimize Artera’s ability to store and serve terabytes of digital pathology data efficiently for the use in serving large-scale training regimes. • Ensure that Artera’s observability infrastructure provides a clear picture of how to continue to optimize performance across our model landscape.

• AI Orchestration: Design and deploy autonomous agents to automate dev processes (e.g., automated PR summaries, synthetic test data generation, and self-healing test suites). • Workflow Automation: Build and maintain complex product workflows leveraging LLMs and RAG (Retrieval-Augmented Generation) to enhance user experiences. • Infrastructure as Code (IaC): Own the cloud architecture using Terraform or Pulumi to ensure GCP resources (Vertex AI, Cloud Run, Cloud SQL) are version-controlled and reproducible. • Full-Stack Development: Maintain and evolve our dual-portal ecosystem: Backend: Node.js, Express, and TypeORM (PostgreSQL). Frontend: React (v17 & v18) using modern state management and routing (Zustand, TanStack). • DevOps Mastery: Implement robust CI/CD pipelines (GitHub Actions/Google Cloud Build) that handle both application code and AI model versioning.