AI Adoption Engineer

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.

Requirements:

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.

Responsibilities:

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.