Navina is a fast-growing digital health SaaS company transforming how physicians interact with patient data. Thousands of clinicians across the US use Navina’s AI-powered solution to turn complex, fragmented records into concise patient portraits and actionable insights at the point of care — reducing burnout, missed diagnoses, and administrative overhead.

As Navina scales its AI product, a parallel opportunity has emerged: applying that same mindset internally. Several non-R&D teams already run AI-powered workflows. What’s missing is a dedicated role to scale this systematically — someone with the technical ability to build and the product judgment to decide what’s worth building.

This role fills that gap: an individual contributor focused on accelerating AI adoption across all non-R&D teams through high-impact tool development, embedded collaboration, and practical enablement.

1. Use Case Discovery & Prioritization

• Embed with non-R&D teams (CS, Sales, Marketing, Finance, HR, Operations) to understand workflows and identify where AI creates the highest leverage.
• Define and prioritize what’s worth building, maintaining a live backlog aligned with VP of AI.

Good discovery is the difference between automation that gets used and automation that gets ignored.

2. AI Tool Development & Delivery (~40%)

• Build custom tools, workflow automations, Cowork skills, and AI integrations with existing business systems.
• Validate with real users, iterate on adoption signals, and transition stable tools to team ownership.

This role leads by shipping. Working tools — not proposals — are the primary output.

3. Enablement & Adoption

• Ensure tools get used: onboarding sessions, playbooks, and hands-on support through behavior change.
• Build team confidence and self-sufficiency over time; avoid creating dependency on a single person.

4. AI Landscape Awareness

• Evaluate new models, tools, and capabilities for internal relevance and bring what matters back to Navina.
• Collaborate with VP of AI to maintain a current roadmap for internal AI operations.

• 3–5 years in a technical role (engineering, solutions engineering, product, or similar) with demonstrated ability to ship software independently.
• Hands-on experience building with LLM APIs (OpenAI, Anthropic, Google, or equivalent).
• Strong product instincts — knows what’s worth building and can make scope decisions without over-engineering.
• Python proficiency: scripting, API integration, automation pipelines.
• Experience with workflow automation tools (Zapier, Make, n8n, or similar).
• Clear communicator across technical and non-technical audiences; self-directed and comfortable with ambiguity.

Optional / Nice-to-Have

• Experience in digital health, healthcare IT, or a regulated industry.
• Familiarity with Claude for Work, Cowork, NotebookLM, or Base44.
• Background in solutions engineering, technical consulting, or B2B SaaS product management.
• Startup or scale-up experience.