• 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.

• Collaborate with engineering teams on the design and implementation of solutions using LLM's • Develop, refine, and optimize prompts to ensure the highest quality and accuracy from AI models • Collaborate with product and engineering teams to integrate AI/ML models and services into our core products • Act as an AI generalist, empowering our internal teams to use AI tooling effectively • Work in a highly collaborative environment with the product and data team to explore new AI technologies • Utilize cloud platforms, particularly AWS AI services, to build, deploy, and manage AI-powered features.