Principal Software Engineer - AI-First Development

8+ years of professional software development experience, including time in senior, lead, or staff positions owning the design and delivery of non-trivial systems.

Demonstrated experience providing technical leadership to a small engineering team, including running code reviews, mentoring engineers, and driving delivery without necessarily holding the formal people-manager role.

Demonstrated daily use, over the past 6 months or more, of at least one modern AI-assisted development tool such as Claude Code, Cursor, GitHub Copilot, or Windsurf, with the ability to speak concretely about effective usage patterns and failure modes.

Strong foundational knowledge in at least one major programming ecosystem (such as .NET/C#, JavaScript/TypeScript, Python, Java, or Go) and the ability to read, evaluate, and validate code in additional languages relevant to a given project.

Working knowledge of relational and non-relational databases, including data modeling, query performance, and schema design.

Experience deploying and operating services on at least one major cloud platform (Azure, AWS, or GCP).

The primary responsibility of the Principal Software Engineer (AI-First Development) is to direct the day-to-day technical execution of a small AI-First engineering team, designing, orchestrating, and validating software applications built through AI-driven development workflows.

This role operates within an AI-First Software Development Lifecycle (SDLC) in which AI agents serve as primary producers of code, configuration, and test artifacts, while the Principal Software Engineer provides architectural direction, context engineering, human-in-the-loop governance, technical mentorship, and final accountability for delivered software.

The Principal Software Engineer is a seasoned engineer who has already integrated modern AI-assisted development tools into their daily workflow and who has experience guiding other engineers through architectural decisions, code reviews, and delivery commitments.

Agent Workflow Design and Orchestration

Define, build, and maintain the AI agent workflows the team uses to produce application code, infrastructure configuration, test suites, and documentation, and guide other engineers in extending them.

Decompose application requirements into discrete, well-scoped tasks that AI agents can execute effectively within defined boundaries, and review task decomposition produced by team members.