AI Operations Associate

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.
• Hands-on technical aptitude: comfortable with APIs, light scripting (Python, JavaScript, or similar), and picking up new tools quickly.
• Exposure to healthcare, operations, or regulated industries is a plus, but not required.
• Strong written and verbal communication; able to translate between technical and non-technical stakeholders and write clearly for leadership.
• Comfort working cross-functionally with Engineering, Product, and Operations, and an ownership mindset about outcomes rather than scope.
• Bachelor's degree or equivalent practical experience.

Nice-to-Haves

• Healthcare, operations, or regulated industries exposure

Compensation & Benefits

• OTE (cash): $100,000-$150,000
• Equity: Competitive equity package
• PTO: Flexible paid time off policy
• Benefits: Health, dental, vision insurance