AI Integration Engineer
at Vega Health · 1-10 employees
Seed · $4M · Oct 2025
- Work model
- Onsite
- Location
- Durham, NC
- Posted
- 5d ago
python
at Vega Health · 1-10 employees
Vega Health provides a curated marketplace and integration platform that helps health systems adopt, implement, and monitor validated AI solutions.
<div class="content-intro"><h2>Overview of Vega Health</h2> <p>At Vega Health, we aspire to be the objective, trusted partner that equips health systems everywhere as they scale AI solutions that improve care and operational outcomes. Inspired by a decade of experience developing, implementing, and validating AI solutions at Duke Health, our team is focused on scaling AI solutions that were developed by those closest to care and partnering with healthcare organizations to integrate innovations that realize outcomes and value. In summary, we founded Vega Health to continuously ask our partners, “What problem can we solve today?” while scaling AI that works.</p> <p>As part of our team, you’ll play a key role in achieving this mission, and you’ll also be building with us, side-by-side and in-person, within the heart of downtown Durham, North Carolina. At the core, we believe in a culture that leads with integrity, trust, and accountability and grows with humble ambition, collaboration, and curiosity. Please visit vegahealth.com for more information about our story and the DNA of how we approach this work.</p></div><p><strong>ABOUT THE ROLE</strong></p> <p>Vega Health is seeking an AI Integration Engineer to identify, design, and integrate AI-enabled solutions that reduce friction and drive operational efficiency across the company. The AI Integration Engineer will work closely with internal teams spanning operations, marketing, partnerships, product, and engineering to embed AI into the day-to-day workflows that power Vega Health’s mission.</p> <p>The AI Integration Engineer will be responsible for uncovering internal pain points, prioritizing projects by business impact, rapidly prototyping and piloting AI-enabled solutions, and leading those solutions through adoption while assessing impact. The ideal candidate is intellectually curious, energized by ambiguity, and skilled at translating complex operational challenges into practical automation. This role requires someone who thrives at the intersection of technology and cross-functional collaboration. The best candidate will operate as a system-level thinker, merging an understanding of business context and value opportunities with the technical knowledge to build, implement, and adapt automated workflows across business functions. They will also be able to structure underlying data and knowledge layers to automate data pulls, synthesis, and recommendation generation instead of simply layering AI on top of manual workflows.</p> <p>This role reports to the Director of Engineering and offers significant room to grow with the company. As Vega Health scales, a successful AI Integration Engineer will have the potential to take on broader ownership of the internal AI capability roadmap. This person should be eager to learn, unafraid to experiment, and genuinely excited about the prospect of making work smarter—not just faster. This is a hands-on, high-ownership role for someone comfortable moving between business discovery, technical prototyping, lightweight integration, vendor evaluation, and user adoption. </p> <h2><strong>WHERE YOU’LL GET TO DIVE IN</strong></h2> <p><strong>Internal Discovery & Problem Scoping</strong></p> <ul> <li>Partner with teams across the company to surface recurring pain points, inefficient manual workflows, and inefficiencies ripe for AI-enabled improvement</li> <li>Conduct discovery sessions to understand current-state processes, identify root causes of friction, and prioritize opportunities based on business/revenue impact and feasibility</li> <li>Act as a connector between functions, ensuring that solutions built for one team can be extended, adapted, or learned from across the organization</li> <li>Collaborate closely with the Engineering team to ensure internal AI tools are built on scalable, sustainable, and cost-effective foundations that align with the company’s broader technical direction</li> </ul> <p><strong>AI Tool Scouting & Evaluation</strong></p> <ul> <li>Continuously monitor the AI tool landscape to identify and propose technical solutions relevant to Vega Health’s internal needs, ideally solving for an enterprise approach and maximizing the potential value from a single tool (rather than implementing multiple, fragmented solutions)</li> <li>Evaluate the balance between external tool procurement and internal solution builds</li> <li>Serve as the team’s resident expert on automation —bringing informed perspectives on new use cases or workflows to internal discussions and decisions</li> <li>Run trials and evaluations to test whether tools actually solve internal pain points by leveraging a rigorous process for assessing the ease of use, reliability, cost, and fit within existing workflows</li> <li>Collaborate closely with the Director of Business Operations and Business Operations Lead as part of the solution procurement and vendor onboarding process, as well as report out anticipated costs for the initial installation and maintenance of tools</li> <li>Take a security and privacy-first approach when evaluating tools for adoption, with recognition that Vega Health prioritizes both in how we work and build </li> </ul> <p><strong>Change Management & Adoption</strong></p> <ul> <li>Drive adoption of AI solutions across functions by partnering with colleagues to embed new capabilities into existing workflows and habits</li> <li>Develop clear documentation, training materials, and onboarding guides that make new tools accessible to colleagues, ensuring that team members are educated on how to effectively leverage AI tools in their daily work</li> <li>Develop a repeatable framework for evaluating and recommending AI tools, ensuring decisions are grounded in evidence</li> <li>Track and communicate the impact of deployed solutions, using data to tell the story of operational improvement and inform future investments </li> <li>Build lightweight integrat