Principal Architect, AI & Platform

Council Capital is a healthcare-focused private equity firm based in Nashville, Tennessee, managing over $350 million in committed capital.

We invest in lower middle market healthcare companies where we see the potential to scale purpose and performance. Our investments span control and minority positions in businesses with enterprise values between $10 million and $100 million.

What sets us apart is the Council Model—a proven framework that surrounds founders and leadership teams with a powerful combination of support: our CEO Council of seasoned operators, our Strategic Healthcare Investors who bring real-world insight and access, and our internal Value Creation Team, focused on enabling growth through talent, systems, and strategy.

At Council Capital, we’re not just backing companies—we’re helping build enduring businesses that improve lives and shape the future of healthcare.

The Role

This is a hands-on senior technical role with multiple connected responsibilities. You will provide architectural guidance across our engineering teams by ensuring services integrate cleanly and evolve without accumulating debt, and you will lead the design and delivery of AI-augmented capabilities: features that help auditors work faster, identify more issues, and make better-supported decisions. Alongside this, you will collaborate with various teams throughout the organization to champion, guide, and support implementations of new AI technologies and their associated processes.

Payment integrity is a high-stakes, regulated domain. Every recovery decision must be defensible in court and subject to audit. This means our current AI strategy emphasizes augmentation and surfacing to make our customers’ lives easier and deliver demonstrable business value. You will bring the judgment to build AI capabilities that work with our auditors, that can be explained to a customer or a regulator, and that hold up when a provider disputes a finding. Bias and performance testing are of similar importance, as we have to prove to our customers that we constantly evaluate both.

You will not have direct reports initially, but you will provide technical leadership across engineering and data science by establishing standards and coaching engineers to align with them. You will review designs and provide concrete, well-supported recommendations on architecture that align with the company’s strategy and vision. Sales and operations will lean on you for recommendations and guidance on improving their processes using AI, so you’ll be expected to keep abreast to how companies have evolved to use this technology to improve productivity and efficiency without sacrificing the important human elements.

What You Will Do

Initial Goals/Technical Roadmap

• Within 1 month, produce a current-state architecture map of our platform.
• Within 2 months, develop a concrete AI integration roadmap with prioritized use cases, sequenced by value and feasibility.
• Within 3 months, collaborate across teams to engineer a full architectural plan for the application and AI platform with diagrams and documentation.
• Within your first 4 months, lead, build, and ship an initial AI-augmented feature as an MVP, working with our product owners, executive leadership, and customers to prioritize the right initial use case. This MVP should be modular and extensible, rather than a one-off feature. Customer feedback is paramount, so you should be prepared to work directly with the customer and our internal stakeholders to align further development with their feedback

Overarching Responsibilities

• Design and deliver AI-augmented capabilities that improve auditor effectiveness, increase operational efficiency, and support better-informed PI and FWA decisions.
• Collaborate across engineering to define and maintain architectural standards the team follows, including API contracts, integration patterns, data flow, and security boundaries.
• Develop and maintain system architecture diagrams.
• Define reusable internal software assets such as shared libraries, APIs, and data contracts that engineering and data science teams can build on. Engineering leads the development and maintenance of these assets; data science contributes where appropriate. The goal is maintainable, flexible components.
• Work with compliance and security to define AI data governance policies which will include what is retained, how long, and for whose benefit.
• Evaluate…