About the Role This is a high-autonomy role at the intersection of customer success and product engineering, where you'll own end-to-end deployments of agentic AI systems for mid-market and large-cap operators. Reporting directly to the C-suite on a lean, fast-growing team, you'll translate complex business problems into scalable AI solutions that deliver measurable outcomes. What You'll Do Own customer deployments from scoping through delivery and ongoing optimization, working directly with operators and finance teams to drive timelines and results. Serve as the primary point of contact for customers, unblocking work and ensuring deployments deliver real business value. Translate ambiguous business problems into structured data models, workflows, and agentic AI systems. Build and extend the agentic AI platform in close collaboration with engineering, surfacing improvements based on customer needs. Support go-to-market efforts by joining sales calls and identifying expansion opportunities within existing accounts. What We're Looking For 3+ years of professional experience in consulting, finance, or internal operations, with a desire to develop technical skills. Proficiency in Python and SQL, with hands-on experience building with AI tools and LLM APIs. Strong background in data analytics, data modeling, and data warehousing concepts. Experience with data pipelines, ETL processes, system integrations, or internal tooling. Highly analytical with comfort working with messy, real-world data and complex systems. Strong communication skills and business intuition; comfortable engaging with executives and operators to gather requirements and deliver solutions. Scrappy, autonomous, and action-oriented — you ship over perfecting. Exposure to private equity, roll-ups, or M&A environments is a plus. US citizenship required; visa sponsorship is not available. Compensation & Benefits Salary range of $110,000–$150,000 USD annually , plus meaningful equity in an early-stage, high-growth company. Location On-site in New York, NY .
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