Principal Engineer & AI-Native Engineering Lead

Flexera saves customers billions of dollars in wasted technology spend. A pioneer in Hybrid ITAM and FinOps, Flexera provides award-winning, data-oriented SaaS solutions for technology value optimization (TVO), enabling IT, finance, procurement and cloud teams to gain deep insights into cost optimization, compliance and risks for each business service. Flexera One solutions are built on a set of definitive customer, supplier and industry data, powered by our Technology Intelligence Platform, that enables organizations to visualize their Enterprise Technology Blueprint™ in hybrid environments—from on-premises to SaaS to containers to cloud.
We’re transforming the software industry. We’re Flexera. With more than 50,000 customers across the world, we’re achieving that goal. But we know we can’t do any of that without our team. Ready to help us re-imagine the industry during a time of substantial growth and ambitious plans? Come and see why we’re consistently recognized by Gartner, Forrester and IDC as a category leader in the marketplace. Learn more at flexera.com
We are building a new model for software engineering: a small, highly autonomous engineering team that combines experienced technical leadership, talented developers, and AI coding agents to deliver high-quality software with exceptional speed.
We are looking for a hands-on Principal Engineer & AI Technical Lead to establish and lead this team. You will be responsible for 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 not a traditional engineering management position. You will remain deeply involved in design, implementation, code review, debugging, and production delivery. Your leverage will come from making sound technical decisions, decomposing complex problems into manageable work, building effective AI-assisted workflows, and enabling a small team to operate with significant independence.
You will own outcomes rather than simply oversee tasks.
What You'll Do:Technical Leadership and Architecture
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
AI-Native Software Engineering
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
End-to-End Delivery and Ownership
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
Quality, Security, and Reliability
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
Team Development and Enablement
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
Required Qualifications: