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Flexera

IT asset management and cloud optimization

Principal Engineer and AI-Native Engineering Lead

Software EngineeringConfirmed live in the last 24 hours
Full-timeRemote in USANo salary listedRemote

About the Job

Requirements

  • 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 the primary technology stacks, with the ability to work across application layers when needed.
  • Deep understanding of testing strategies, continuous integration and continuous delivery (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.
  • 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.

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 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 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 delivery results rather than AI usage or code-generation volume.
  • 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 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.
  • Automate quality controls 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.
  • Delegate meaningful ownership and create reusable patterns and automated guardrails to avoid becoming a bottleneck.
  • Share successful AI engineering practices with the broader engineering organization.

Desired 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 Flexera's relevant industry, domain, and technology stack.
  • Experience improving delivery lead time, software quality, operational reliability, or engineering productivity through measurable changes.

About the Company

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Flexera

IT asset management and cloud optimization

Flexera provides IT management software and optimization tools that help businesses control software and IT costs. Its product suite includes IT asset management (ITAM) and software asset management (SAM) to track software usage and licenses, along with cloud management solutions to optimize cloud resources. Flexera’s TVO (Technology Value Optimization) approach aims to maximize the business value of every dollar spent on technology, ensuring assets are used efficiently and cost-effectively. The company differentiates itself through its integrated suite of asset-management and cloud-management tools and by expanding capabilities with acquisitions like Snow Software, which broadens its ability to help clients optimize technology investments. The goal is to help large organizations grow efficiently, achieve ROI on technology expenditures, and navigate digital transformation by controlling costs and optimizing technology investments.

Company size
1,001-5,000
Company stage
Debt Financing
Total funding
$3B
Headquarters
Itasca, IL
Founded
2008

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