Principal Engineer & AI-Native Engineering Lead

Technical Leadership and Architecture

AI-Native Software Engineering

End-to-End Delivery and Ownership

Quality, Security, and Reliability

Team Development and Enablement

Required Qualifications:

• Significant professional software engineering experience, typically 8+ years, with demonstrated technical leadership appropriate to a Principal or Staff+ engineer
• Strong software design and architecture skills, with 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 of our primary technology stacks, with 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
• A track record of taking ownership of outcomes rather than limiting responsibility to assigned implementation tasks

Preferred Qualifications:

• 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 our relevant industry, domain, and technology stack
• Experience improving delivery lead time, software quality, operational reliability, or engineering productivity through measurable changes
• Flexera is proud to be an equal opportunity employer. Qualified applicants will be considered for open roles regardless of age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by local/national laws, policies and/or regulations.
• Flexera understands the value that results from employing a diverse, equitable, and inclusive workforce. We recognize that equity necessitates acknowledging past exclusion and that inclusion requires intentional effort. Our DEI (Diversity, Equity, and Inclusion) council is the driving force behind our commitment to championing policies and practices that foster a welcoming environment for all.
• We encourage candidates requiring accommodations to please let us know by emailing [email protected].

#LI-Development

Responsibilities

• Own the 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
• Make sound trade-offs between delivery speed, reliability, security, scalability, and long-term maintainability
• Establish lightweight engineering standards, architectural patterns, coding conventions, and documentation practices
• Identify opportunities to simplify systems, eliminate unnecessary complexity, and accelerate delivery
• Ensure the team builds software that can be understood, tested, operated, and evolved by engineers beyond its original authors
• Make AI coding agents a first-class part of the development lifecycle, using tools such as Claude Code, OpenAI Codex, and other appropriate AI engineering tools
• Develop effective workflows for AI-assisted requirements analysis, code generation, test creation, debugging, refactoring, code review, and documentation
• Decompose work into clear, bounded tasks that AI agents can execute independently, in parallel, and with measurable results
• Establish project context, repository instructions, reusable prompts, agent skills, automated checks, and other mechanisms that improve the consistency of AI-generated work
• Evaluate AI-generated code critically, recognizing when to accept, modify, or reject its output
• Continuously improve the team's engineering workflow based on actual delivery results rather than AI usage or code-generation volume
• Take responsibility for delivering 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 larger initiatives into small, independently testable increments that can be delivered frequently
• Identify and resolve technical blockers without introducing unnecessary meetings, dependencies, or handoffs
• Establish appropriate observability, deployment automation, rollback procedures, and operational safeguards
• Monitor production outcomes and use feedback to improve the product and engineering process
• Define a practical quality strategy covering automated testing, code review, integration testing, and production validation
• Ensure AI-generated changes are verified through appropriate tests, 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 create unacceptable security, reliability, compliance, or maintainability risks
• Make quality controls increasingly automated so that a small team can move quickly without relying on manual inspection for every routine check
• Lead by example through hands-on engineering, thoughtful technical decisions, and disciplined execution
• 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
• Avoid becoming a bottleneck by delegating meaningful ownership and creating reusable patterns and automated guardrails
• Share successful AI engineering practices with the broader engineering organization