Senior Software Engineer

As a Senior Software Engineer within Next Gear Capital
, you will play a critical role in designing, building, and evolving the applications, platforms, and digital capabilities that support one of the automotive industry's leading inventory finance businesses. Working at the intersection of technology, product, and business strategy, you'll help deliver solutions that improve operational efficiency, enhance customer experiences, and enable the organization's continued investment in innovation and modernization.

This role is ideal for an experienced engineer who thrives in complex environments and enjoys turning ambiguous business challenges into scalable, maintainable software solutions. You will partner closely with product managers, architects, engineers, and business stakeholders to define technical approaches, decompose complex initiatives, and drive delivery of high-quality software across the full development lifecycle.

As a senior member of the engineering organization, you will be expected to contribute beyond feature implementation by influencing development practices, mentoring teammates, raising engineering standards, and helping guide technical decision-making across products and services. You'll regularly balance short-term delivery objectives with long-term maintainability, scalability, and operational excellence.

At Next Gear Capital, AI-assisted software development is becoming a foundational component of how engineering teams operate. In this role, you will help shape and mature those practices by effectively leveraging modern AI tools throughout the Software Development Lifecycle (SDLC), sharing best practices, refining team workflows, and ensuring AI-augmented development remains grounded in sound engineering judgment, quality, security, and accountability.

Success in this position requires strong full-stack engineering capabilities, a passion for continuous improvement, and the ability to influence outcomes through technical expertise, collaboration, and execution. You'll have opportunities to contribute to architectural discussions, drive engineering improvements, mentor fellow developers, and help define the future of software development within an organization actively investing in AI-driven transformation and modern engineering practices.

• Collaborate with AI agents and tools to build, test, and deploy software across the SDLC, providing proper contextual inputs to improve AI understanding and output quality
• Define coding standards, review practices, and ethical guidelines for AI use
• Mentor peers and coach junior team members on AI-augmented development
• Serve as a participating member on teams tasked to develop, improve, and deliver product/system elements and features
• Break down functional and business requirements into tangible, clear, and estimable objectives, providing recommendations to management and project team members
• Own Spec Driven Development (SDD) sessions with the team, turning ambiguous requirements into structured, actionable specs
• Coach teammates on how to break work down into clear, estimable objectives themselves, rather than doing it for them every time
• Ensure quality, performance, and adherence to specifications of assigned development tasks and adjacent technology solutions
• Establish and improve observability practices (logging, metrics, tracing, alerting) across owned systems, using them to proactively identify risk, guide architectural decisions, and diagnose issues before they impact clients
• Work with fellow team members to ensure design, development, and execution align with and support adjacencies and adhere to established architectural standards
• Notify the team or technical manager of variances from plans and identify changes in scope or work
• Help define and support implementation of coding standards
• Offer technical guidance to less experienced team members
• Participate in code reviews, both giving and receiving feedback

• Working Software, Every Sprint:
  Produce working software each sprint that meets the definition of done, delivers expected value, and stays within constraints defined by engineering leadership, with solution complexity broadly understood well enough to drive consistent, meaningful estimates across the team
• AI Fluency & Standards:
  Drive AI-augmented workflows and standards (coding, review, ethical use) that measurably improve team speed and quality. Mentor others in adopting them
• Quality Solutions:
  Deliver maintainable, testable, and performant software that leverages existing patterns, is sufficiently abstract, and meets client needs across adjacent systems
• Craft Growth & Mentorship:
  Continue pursuing engineering craft mastery while actively growing the skills of less experienced teammates
• Plan Transparency:
  Ensure changes in direction that affect time, effort, cost, or scope are surfaced to the team and above with urgency that maximizes the team's ability to replan or pivot
• Initiative:
  Identify opportunities for automation, improvement, or…