council-capital - Principal Architect, AI & Platform
Requirements
• 10+ years architecting SaaS products (not single-use internal applications) with cloud-native technologies. • Extensive experience in cloud-native design on AWS and/or Azure. • Proven track record of integrating and building AI into platforms and applications, going beyond simple API usage. • Hands-on familiarity with the Model Context Protocol (MCP); ideally with implementation experience. • Hands-on experience with packaged agent runtimes (e.g., Bedrock AgentCore or Azure AI Foundry), not just self-built orchestration. • Experience designing logically multi-tenant SaaS platforms. • Strong software fundamentals combined with ongoing learning of emerging AI patterns and protocols. • Proven ability to communicate adeptly with internal and external stakeholders at the right level of technical detail given the individual. • Sound judgment to instinctively build for extensibility, security, and sane deployment, and the ability to speak authoritatively to standards through experience. • The ability to move quickly and decisively, and to unite engineers rather than working in isolation. • Systems and storage architecture across multiple integrated platforms. • Experience in healthcare or other regulated, high-compliance environments. • Experience standing up a platform quickly for internal development teams (developer experience). • Council Capital and our portfolio companies are committed to building high-performing teams by hiring the best talent—period. • We believe in putting the right people in the right seats, regardless of background, and we’re always looking for individuals who bring fresh thinking, grit, and a drive to make a difference. • Thank you for considering a role with one of our companies. We’re excited to learn more about you.
Responsibilities
• 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. • 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 • 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 all engineering and AI tooling and vendors pragmatically: weigh capability against cost, vendor lock-in, and operational complexity. We are primarily in AWS but do have some Azure footprint, and we expect you to evaluate their capabilities against other options and make recommendations. • Build appropriate evaluation and bias testing methodologies. • Work with sales and operations to understand their current processes and recommended areas where AI can augment and assist them.
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