Principal Engineer & AI-Native Engineering Lead

Flexera

19h•Remote

About The Position

Flexera is building a software engineering model centered on a small, highly autonomous team of experienced technical leadership, developers, and AI coding agents. The Principal Engineer & AI-Native Engineering Lead will establish and lead this team, owning technical direction, architecture, engineering quality, and delivery outcomes while actively developing software alongside two supporting engineers and AI tools such as Claude Code and OpenAI Codex. This is a hands-on technical leadership role rather than a traditional engineering management position. The lead remains involved in design, implementation, code review, debugging, and production delivery. The role's impact comes from sound technical decisions, breaking complex problems into manageable work, building effective AI-assisted workflows, and helping a small team work independently and deliver production-ready software.

Requirements

Typically 8 or more years of professional software engineering experience, with technical leadership appropriate to a Principal or Staff+ engineer.
Strong software design and architecture skills, including the ability to balance simplicity, delivery speed, maintainability, and operational needs.
Demonstrated experience delivering and operating production software end to end.
Strong programming skills in at least one primary technology stack and the ability to work across application layers when needed.
Deep understanding of testing strategies, CI/CD, version control, observability, application security, and production troubleshooting.
Practical experience using AI coding agents such as Claude Code, OpenAI Codex, or comparable tools in real engineering workflows.
Ability to break ambiguous business problems into well-defined technical tasks and independently drive them to completion.
Excellent technical communication, problem-solving, mentoring, and decision-making skills.
Track record of taking ownership of outcomes rather than limiting responsibility to assigned implementation tasks.

Nice To Haves

Experience building or leading small, autonomous, cross-functional engineering teams.
Experience with agentic coding workflows, parallel AI task execution, repository-level instructions, and automated validation pipelines.
Experience establishing internal developer platforms, reusable engineering templates, or AI-enabled development infrastructure.
Experience in Flexera's relevant industry, domain, and technology stack.
Experience measurably improving delivery lead time, software quality, operational reliability, or engineering productivity.

Responsibilities

Own technical architecture, design decisions, and implementation strategy for the team's applications, services, and systems.
Translate business objectives and product requirements into pragmatic, maintainable technical solutions.
Balance delivery speed, reliability, security, scalability, and long-term maintainability.
Establish lightweight engineering standards, architectural patterns, coding conventions, and documentation practices.
Simplify systems, eliminate unnecessary complexity, and accelerate delivery.
Ensure software can be understood, tested, operated, and evolved by engineers beyond its original authors.
Integrate AI coding agents into the development lifecycle and develop workflows for AI-assisted requirements analysis, code generation, testing, debugging, refactoring, code review, and documentation.
Break work into clear, bounded tasks that AI agents can execute independently or in parallel, with measurable results.
Establish repository context and instructions, reusable prompts, agent skills, automated checks, and other mechanisms to improve AI-generated work.
Evaluate AI-generated code and continuously improve engineering workflows based on delivery outcomes.
Deliver production-ready capabilities from problem definition through deployment and operational support.
Collaborate with business stakeholders, product owners, users, and other engineering teams to clarify requirements and prioritize work.
Break initiatives into small, independently testable increments and resolve technical blockers without unnecessary meetings, dependencies, or handoffs.
Establish observability, deployment automation, rollback procedures, and operational safeguards; monitor production outcomes and use feedback to improve the product and process.
Define a quality strategy covering automated testing, code review, integration testing, and production validation.
Ensure AI-generated changes receive appropriate testing, static analysis, security checks, and human review.
Establish guardrails for AI access to source code, credentials, production systems, customer data, and other sensitive resources.
Prevent shortcuts that introduce unacceptable security, reliability, compliance, or maintainability risks, and automate routine quality controls where appropriate.
Mentor two supporting engineers through pairing, code reviews, design discussions, and progressively more challenging assignments.
Build a culture of ownership, experimentation, direct communication, accountability, and continuous learning.
Help engineers develop the judgment to direct AI agents, validate their work, and solve problems independently.
Delegate meaningful ownership, create reusable patterns and automated guardrails, and share successful AI engineering practices with the broader engineering organization.