Principal Architect, AI-Native Developer

Please Note:

This is a remote position available in the state(s) listed on this job. Additionally, employment with BambooHR is contingent on passing both a background and credit check.

AI at BambooHR At BambooHR, we believe in leveraging cutting‑edge technology to empower people and transform HR. We’re actively integrating AI into our solutions and workflows to enhance efficiency and drive innovation. To that end, we’re looking to our existing team members and future hires to share this forward‑thinking mindset: individuals who are curious about AI’s potential, eager to learn and adapt, and ready to explore how intelligent tools can elevate their work along with BambooHR’s impact on setting people free to do great work.

Join us in reimagining the future of HR!

Agents now write, review, test, and ship code alongside engineers. As Principal Architect for AI‑Native Developer Experience, you will lead the architecture for how BambooHR engineers and agents build software together, from the tools and context agents rely on to the pipelines that validate their work. We are looking for someone who is endlessly curious, stays at the forefront of AI and software technology, and knows when to think a problem through and when to settle it with a working prototype.

This is a hands‑on role on an award‑winning product, with a team that lives our Bamboo Values, at a company that believes you should have a life outside of work.

You will:

• Set the direction. Define the technical vision, reference architecture, and incremental roadmap for AI‑native development, and document key decisions in ADRs.
• Experiment and prove it out. Track emerging models, agents, and developer tools, run time‑boxed experiments and proofs of concept, and turn what works into supported patterns while retiring what does not.
• Establish and champion agentic engineering practices. Define how engineers work with coding agents (spec‑driven development, context engineering, review of agent‑generated code), build a shared library of skills, tools, and MCP servers, and evangelize these practices across engineering through demos, playbooks, and communities of practice.
• Automate the SDLC with agents. Architect agentic workflows from triage through implementation, review, testing, and remediation, with autonomy levels and human‑in‑the‑loop guardrails set by risk and backed by evals.
• Build the knowledge layers. Give agents and engineers accurate, current context through engineering and product knowledge layers: code intelligence, service catalogs, architecture records, domain models, and business rules.
• Make domains agent‑ready. Define the standard for agent‑ready domains (clear boundaries, documented APIs, agent‑callable tools, fast tests, fleeting environments) and help teams close the gaps.
• Reimagine CI/CD for the AI era. Redesign the pipeline for the volume of agent‑authored changes, with intelligent test selection, merge queues, automated quality and security gates, and progressive delivery.
• Rethink the core developer experience. Modernize development environments, onboarding, documentation, and the internal developer portal so they serve engineers and agents alike, and measure developer experience and productivity to guide where we invest.
• Govern for trust and cost. Partner with Security, Privacy, and Quality Engineering on agent permissions, sandboxing, and audit trails, and track AI cost and quality so gains hold up.
• Lead hands‑on and through others. Write production code and reference implementations, partner closely with architects and staff engineers across domains to set direction and land changes, and mentor engineers as they build agentic skills.

What You Need to Get the Job Done

• Bachelor’s degree in computer science or a related field, or…