Manager, AI-Native Software Engineering

When life gets hard, we make it easier! Libra Solutions helps overcome the burdens created by slow-moving legal processes. Combining technical innovation and financial strength, we help speed up cumbersome workflows and ease financial barriers for our customers. Through the MoveDocs personal injury solutions platform, Libra integrates and streamlines medical, financial, and professional services for personal injury cases. Our mission is to help personal injury victims receive the medical care and personal funding needs they require, and to help streamline the process for the attorneys and medical providers that serve these victims. Libra operates under the MoveDocs, Oasis Financial and Probate Advance brands. We are proud of our mission and passionate about applying technology to the challenge of making healthcare more accessible. Together, under the Libra Solutions banner, we have relationships with over 50,000 attorneys and over 12,000 healthcare providers nationwide, which gives us an amazing platform to service our customers. Job description The Manager, AI-Native Software Engineering leads a team of engineers building and maintaining the platforms that power Libra Solutions’ products within an agentic delivery model. This role combines people leadership with technical oversight, and is accountable for delivering high-quality software, growing the team’s AI-native capabilities, and ensuring the team operates effectively in a pod model where AI agents are active participants in the SDLC alongside human engineers. The ideal candidate is a player-coach who manages and develops a team effectively while staying connected to the technical work. They bring strong engineering depth in the Microsoft / Azure stack, proven people leadership skills, and demonstrated experience leading teams. This role partners closely with Engineering, Product, and business stakeholders to delivery high-quality software at scale. This position reports to the Director of Engineering and can be based in any of our office locations in Denver, CO, Huntersville, NC, Rosemont, IL or Las Vegas, NV. We welcome strong remote candidates, with occasional travel to Las Vegas as needed. Position Responsibilities: Team Leadership and People Management Manage, mentor, and develop a team of software engineers across all experience levels Conduct regular 1:1s, provide timely and constructive feedback, and support each engineer’s professional growth and career development Partner with recruiting to grow the team by attracting and hiring engineers aligned with the team’s AI-native technical direction Set clear expectations, foster accountability, and build a culture of trust, collaboration, and continuous improvement Address performance issues constructively and proactively Engineering Delivery Own the team’s delivery commitments; ensure features, bug fixes, and technical investments are completed on time and meet quality standards Participate in sprint planning, backlog refinement, and prioritization discussions with Product and stakeholders Remove blockers, manage dependencies, and ensure the team has what it needs to execute effectively Monitor and continuously improve team velocity, predictability, and delivery health within the agentic pod model Technical Oversight and Practice Maintain strong technical engagement: participate in architecture discussions, code reviews, and technical planning Ensure the team adheres to engineering standards for C# / .NET, API design, database patterns, front-end development, and CI/CD using Azure DevOps Identify and address technical debt; champion sustainable engineering practices Drive adoption and effective use of AI-assisted development tools (GitHub Copilot, Claude, Azure OpenAI) across the team; ensure engineers are working in an agentic delivery rhythm where AI handles generation at scale and engineers own judgment, quality, and architectural decisions Partner with other engineering leaders to define and uphold organizational AI development standards, prompt engineering practices, and AI output review requirements Metrics and Continuous Improvement Track and evaluate team-level engineering metrics, including sprint velocity and predictability, CI/CD pipeline health, production incidents, defect backlog trends, and system performance data to identify systemic issues and drive continuous improvement Use data to communicate engineering health, technical decisions, tradeoffs, and statuses clearly to stakeholders and leadership Contribute to engineering-wide initiatives including hiring practices, standards, tooling, and process improvement