AI Operations Associate

Flagler Health is building the clinical operating system for modern musculoskeletal care. We partner with MSK provider groups and specialty clinics to help them grow, operate more efficiently, and deliver better longitudinal care across patient acquisition, clinical workflows, and ongoing patient engagement. Our platform sits at the intersection of care delivery and clinic operations, helping providers capture more value across the full patient lifecycle.

We’ve recently raised our Series B and are entering our next phase of growth.

Role We are hiring an AI Operations Associate to design, build, and scale the AI and automation layer that powers Flagler Health’s back-office and clinical operations. This leader will help take the company from ad-hoc scripts and manual workflows to a durable, systematic AI ops engine that compounds in value as the company grows.

Reporting to the Co-Founder & CEO, Albert Katz, and partnering closely with our Co-Founder & CTO Will Hu this leader will own the AI & Automation function while working across BizOps, RCM, and Product to drive measurable operational leverage. This is not a “sit back and manage” role. As AI Operations Associate, you will operate as a player-coach—shipping automations from the front while building the systems, standards, and team that scale them.

You will own the company’s automation roadmap, refine and operationalize how we capture and translate workflows into production systems, and help evolve the operating model to support more complex EHR integrations, larger provider footprints, and continued expansion across multi-site organizations. This is a high-impact role at a critical inflection point, with the opportunity to shape how Flagler scales its operational backbone.

Location: Based in New York City.

Key Responsibilities

• Automation Ownership: Own the company’s automation roadmap and consistently deliver measurable operational leverage against it.
• AI Ops Scaling: Stand up and scale the AI Operations function, with responsibility for hiring, onboarding, and developing additional operators and engineers as the company grows.
• Workflow Discovery: Stay close to the field by shadowing clinical and back-office teams to deeply understand workflows and identify the highest-ROI automation opportunities.
• Automation Playbooks: Refine and operationalize repeatable, scalable processes for capturing workflows, shipping automations, and monitoring them in production.
• EHR Integrations: Lead data integrations across major EHRs (eClinicalWorks, Athena, NextGen, and others) and own the contracts, error handling, and reliability of those pipelines.
• Tooling Strategy: Evolve the automation stack to match the workflow—APIs and portal integrations where possible, browser and desktop automation where necessary, and screen-level robots for last-mile flows.
• AI Enablement: Stand up and maintain internal AI tooling (LLMs, agent frameworks, knowledge assistants) and train teammates to become power users of AI in their daily work.
• Vendor Management: Lead scoping and execution with automation vendors, holding them to delivery, quality, and reliability standards.
• Cross-Functional Alignment: Partner closely with Engineering to align on integration patterns, data contracts, and what belongs in product vs. ops.
• Team Leadership: Coach and develop the team on AI tools through structured training, hands-on support, and ongoing guidance.
• Hiring & Scaling: Continue building a high-performing AI Ops team with strong culture, accountability, and execution discipline.
• Operational Discipline: Enforce rigorous documentation, monitoring, and change-management practices to support data-driven decision-making and reliable production systems.
• Customer Voice: Serve as a key conduit between operators, clinicians, and internal teams, informing product and roadmap strategy with workflow-level insight.

Requirements

• 1–3 years of experience in management consulting, software engineering, product management, or a similar analytical/technical role — ideally with exposure to high-growth or ambiguous environments.
• Strong problem-solving instincts with a bias toward action: comfortable scoping an ambiguous problem, breaking it down, and shipping a working solution end-to-end.
• Genuine fluency with modern AI tooling — LLMs, agent frameworks, prompt engineering — and a clear sense of where AI works well versus where it breaks down.
• Han…