Manager, AI-Native Software Engineering

Libra Solutions•Las Vegas, NV

2h•Hybrid

About The Position

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.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or related field, or equivalent professional experience
7+ years of professional software development experience
2+ years of people management or team leadership experience
Strong hands-on background in C# / ASP.NET Core, Entity Framework Core, SQL Server / Azure SQL, and React or Angular
Experience with Azure DevOps, CI/CD pipelines, and cloud-native deployment practices on Azure
Experience partnering closely with engineering, product, and business stakeholders in an agile delivery environment
Demonstrated ability to lead, develop, and retain a high-performing engineering team
Strong problem-solving, communication, organizational, and coaching skills
Hands-on familiarity with AI-assisted development tooling (GitHub Copilot, Claude, Azure OpenAI) and a demonstrated commitment to building AI-native engineering practices; able to coach engineers on effective prompt engineering, AI output evaluation, and responsible AI use
Experience leading teams within agentic or AI-accelerated delivery models, or a clear understanding of how agentic workflows change team rhythms, role expectations, and quality ownership

Responsibilities

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
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
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
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