About the Company

We are seeking a Research & Development Manager to lead the transformation of our engineering organization into an AI-first delivery engine. This is a critical leadership hire at a pivotal moment: we are executing a platform modernization effort, migrating legacy codebases, and accelerating product velocity through an AI-enabled Development Lifecycle (AI-DLC).

About the Role

The Manager of R&D will own the R&D vision, drive measurable improvements in delivery speed and product quality, and build the team and practices required to win in a competitive market. This role combines strategic leadership with hands-on technical direction. You will lead a mixed team of internal engineers and embedded partner resources, manage platform modernization dependencies, and ensure we deliver consistent, high-quality product value to customers. You are equally at home navigating complex legacy systems and driving adoption of modern AI-enabled development practices — and you bring the experience and judgment to know when each approach is warranted.

Responsibilities

AI-DLC Transformation & Innovation Lead the transition from SDLC to AI-DLC. Embed AI tooling, automated testing, model-assisted development, and intelligent automation into the product development lifecycle in a deliberate, sustainable way. Establish best practices for AI-assisted product development, including validation, quality assurance, and continuous improvement cycles. Balance modernization ambition with operational stability. Ensure the team executes at higher velocity without sacrificing reliability or institutional knowledge.

Technology Strategy & Architecture In collaboration with Product, define and execute the long-term technology roadmap, including platform consolidation, legacy migration, and modernization priorities. Oversee architecture for a scalable, maintainable platform capable of supporting AI-native features. Lead platform re-architecture and legacy modernization efforts. Establish the product documentation and knowledge base required to support long-term platform health. Make sound architectural decisions under uncertainty. Bring the experience to distinguish between incremental improvements and structural investments — and the discipline to sequence them correctly.

Product & Business Alignment Partner with Product, Sales, and Customer Success to deliver solutions that address competitive gaps and create durable customer value. Translate vertical market use cases into scalable, reliable technical solutions. Align R&D priorities with company growth objectives and deliver quarterly major product releases consistently.

Governance, Security & Trust Implement AI governance practices covering model reliability, data privacy, and responsible deployment. Ensure appropriate product security and compliance standards for a customer-data platform. Promote engineering practices that build customer trust. Understand that in relationship-driven markets, technical reliability has direct business consequences.

Leadership & Team Development Lead a mixed team of internal engineers and embedded partner resources. Set clear expectations, maintain accountability, and develop talent at all levels. Foster a culture of documentation, knowledge-sharing, and continuous improvement. Actively reduce tribal knowledge concentration. Recruit and develop technical talent across experience levels. Balance the need for deep domain expertise with adaptability to new tools and practices. Maintain team morale and cohesion through organizational change. Communicate with clarity and consistency — up, down, and across.

Operational Excellence Improve delivery speed, reliability, and product quality through CI/CD, automation, and observability practices. Support and reinforce an established weekly release cadence. Increase release predictability over time. Drive engineering performance through measurable KPIs. Report clearly to the President and executive leadership. Success Measures (First 6 Months)

AI-DLC framework fully implemented and adopted across all active R&D workstreams. Product documentation and knowledge base established, sufficient to support platform modernization and reduce tribal knowledge risk. Quarterly product release cadence maintained with improved predictability. Measurable improvement in engineering delivery speed against established baseline. R&D team operating with clear cross-training practices in place and no critical knowledge concentration risk. Legacy migration roadmap confirmed and on track.

Qualifications

Required 10+ years of experience in software engineering, R&D, or product development — with meaningful time spent working in and modernizing legacy systems. 5+ years in a senior technical leadership role managing mixed teams of internal and external engineering resources. Demonstrated experience navigating the full technology maturity spectrum: from maintaining and evolving legacy platforms to leading adoption of modern and AI-enabled development practices. Strong expertise in system architecture, with a track record of making sound decisions in complex, resource-constrained environments. Proven ability to improve engineering performance through modern development practices (CI/CD, automation, observability) without disrupting operational continuity. Clear, direct executive communication skills. Comfortable presenting to financially literate leadership and operating within structured corporate governance models. The judgment and credibility that comes from having done this before, not just managed people who have.

Preferred Experience in vertical market or relationship-driven B2B software. Deep familiarity with legacy codebase modernization, including the organizational and cultural challenges that accompany it. Experience managing outsourced or embedded development partners alongside internal teams. Experience within a Constellation Software (CSI) or similar PE/holding company operating model. Leadership Profile

Seasoned and grounded. Brings perspective that only comes from years of navigating real engineering complexity — not just theoretical frameworks. Execution-focused. Able to move from strategy to delivery without losing either. Strong business acumen. Connects technology decisions directly to product value, customer retention, and company performance. Operates with clarity and low ambiguity, especially in uncertain, resource-constrained environments. Builds trust quickly with both technical teams and non-technical stakeholders. Leads with stability. Understands that in a transformation, steady and credible leadership matters as much as technical vision. Comfortable with the realities of a lean, corporate-governed operating model: disciplined, metrics-driven, and accountable. Committed to building high-performing, accountable teams, including in remote and distributed settings.