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Midstream is an AI-native financial operating system for health systems that provides real-time visibility into margin risk and actionable insights for finance, supply chain, and managed care teams. This role bridges Sales, Product, Engineering, and Customer Success as a technical advisor supporting enterprise healthcare customers.
You will lead technical discovery and solution architecture for prospective customers, helping them understand how Midstream integrates into complex healthcare environments. You'll guide executive stakeholders, enterprise architects, and security teams through the customer journey—from initial discovery through implementation planning—serving as a trusted technical advisor on AI adoption, enterprise architecture decisions, and integration approaches.
Key responsibilities include: conducting technical discovery sessions to understand customer landscapes and integration requirements; designing enterprise solutions and phased deployment strategies; leading architecture reviews, executive workshops, and security discussions; developing AI evaluation frameworks and governance models for healthcare organizations; building and maintaining scalable technical assets including demonstration environments, reference architectures, and implementation playbooks; partnering with Product and Engineering to identify implementation patterns that inform product strategy; and creating repeatable technical messaging and methodologies that accelerate enterprise sales cycles.
You should be passionate about applied AI and responsible AI adoption, with deep experience as a Solutions Architect or Sales Engineer supporting complex enterprise software. You're a systems thinker comfortable with APIs, cloud architecture, enterprise integrations, data engineering, and AI workflows. Exceptional communication skills are essential—you move fluidly between executive strategy conversations, technical architecture discussions, and live demonstrations. You're a builder who creates reusable assets that elevate the entire organization, and you're customer-obsessed about reducing risk and improving the product. Experience with enterprise AI/ML deployments, modern LLMs, AI agents, and evaluation frameworks is preferred.
