Infrastructure Engineer

Description HOAi is a fast-growing startup revolutionizing the community association management industry. Our AI workforce platform integrates machine learning technology to streamline labor-heavy processes, eliminating inefficiencies and driving scalability. With rapid growth in the AI space, we are pushing boundaries to redefine industry standards. HOAi is the leading AI solution for the community association management industry, enabling organizations to deploy AI Agents that function like experienced managers. These AI Agents go beyond traditional AI by proactively executing complex, multi-step processes with human-like reasoning—working autonomously, 24/7, across your entire operation. This transformation optimizes labor costs, enables growth without additional hires, and ensures faster, higher-quality service for residents and board members. HOAi was acquired by Vantaca in the fall of 2024. Vantaca just achieved unicorn status with a $1.25B valuation, so it's safe to say we're past the "scrappy startup phase." We're not just building a successful company – we're building the category-defining platform that will transform how an entire industry operates. Here's the reality of our trajectory: Growing 100% year-over-year Our AI product (HOAi) went from $0 to millions in months Backed by Cove Hill Partners and JMI Private Equity 6M+ doors on our platform, displacing l [... source excerpt omitted ...] operate with exceptional reliability, performance, and efficiency. The ideal candidate is someone who gets excited about making AI systems fundamentally faster and more scalable. You'll work directly with our engineering and product teams to build the foundational infrastructure that enables HOAi to deliver the most advanced AI product in the community association management industry. Accountability Key Initiatives Infrastructure Ownership: Design, build, and maintain the cloud architecture, model serving infrastructure, and ML pipelines that power HOAi's products Performance Optimization: Profile and optimize AI workloads to achieve sub-second inference latency while manag [... source excerpt omitted ...] sure 99.99% uptime for mission-critical AI services MLOps Excellence: Develop and maintain CI/CD pipelines for model deployment, monitoring, and versioning across development and production environments Developer Enablement: Create tooling and infrastructure that allows product engineers to deploy AI features quickly and safely Security & Compliance: Implement security best practices and ensure compliance requirements are met across all AI infrastructure Expectations for Success Infrastructure uptime and reliability AI inference latency (p95, p99) and throughput metrics Infrastructure cost efficiency and optimization (cost per inference, GPU utilization) Time to deploy new