Overview
Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy sof
Full job description
Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai. Follow Shield AI on LinkedIn, X, Instagram, and YouTube.
Why This Role Exists: We build and operate the platform that our engineers and our customers rely on. The platform isn't a product that's ever "finished." It's something we improve continuously. Every deployment, outage, bug, and annoying manual task is an opportunity to make the system better. An L3 Platform Engineer is a fully contributing member of that effort. This isn't a ticket factory. We expect engineers to think critically, collaborate closely, and continuously improve both the platform and the way we build it.
Our engineering culture is built on Extreme Programming and Continuous Delivery.
That means:
Pair programming is our default way of developing software.
We work in small batches.
We integrate continuously.
We automate repetitive work.
We test first whenever practical.
We optimize for learning and feedback instead of individual velocity.
The team owns the outcome.
If you're looking for a role where you're handed tickets, disappear for a week, and come back with a pull request, this isn't it.
Build and improve the platform that deploys and operates customer environments.
Develop infrastructure as code using Ansible, Terraform, Helm, Zarf, Big Bang, Packer, and related tooling.
Improve Kubernetes platforms and the systems around them.
Build deployment automation that reduces risk and removes manual work.
Pair with other engineers to design, implement, and troubleshoot platform capabilities.
Write automated tests for infrastructure and deployment workflows.
Improve observability, reliability, security, and recoverability.
Write documentation that explains why something exists—not just how to type commands.
Leave every part of the system better than you found it.
You Build for Change
Software is never finished.
Your goal isn't simply to make today's feature work.
Your goal is to leave the code, infrastructure, and deployment process easier to change tomorrow.
You Prefer Automation
If humans perform the same task repeatedly, the system probably needs improvement.
Manual processes are temporary.
Automation is the destination.
You Think in Small Batches
Large changes hide mistakes.
Small changes expose them.
We value engineers who can decompose large problems into safe, incremental improvements.
You Work as Part of the Team
Platform engineering is a collaborative discipline.
You bring potential solutions to the team, clearly explain your reasoning and tradeoffs, and help the team build a shared understanding of the code or design not merely produce it. You are responsible for understanding, validating, and clearly communicating the work you contribute.
You Improve the System
When something goes wrong, we don't stop at fixing it.
We ask:
Why did this happen?
Why wasn't it detected sooner?
How do we make this impossible or at least unlikely next time?
Incidents should improve the platform.
You should be comfortable working across most of these areas:
Kubernetes
Linux
Networking fundamentals
Git and trunk-based development
GitLab CI
Infrastructure as Code
Ansible
Terraform
Helm
Zarf
Containers
PKI and certificate management
Secrets management
Observability
Infrastructure testing
Troubleshooting distributed systems
No one is expected to know everything.
We do expect you to learn quickly and become productive in unfamiliar systems.
Successful L3 engineers consistently help the team:
Deploy more frequently.
Deploy more safely.
Recover from failures faster.
Remove manual work.
Reduce operational complexity.
Improve documentation.
Increase confidence through testing.
Deliver changes in small, reversible increments.
Make the platform easier to operate and easier to evolve.
Simplicity over cleverness.
Evidence over opinion.
Learning over ego.
Automation over repetition.
Continuous improvement over perfection.
Team outcomes over individual heroics.
Building knowledge silos.
Defending "the way we've always done it."
Optimizing for personal productivity instead of team throughput.
Big-bang rewrites when incremental change will work.
Manual work that should be automated.
Complexity without a clear operational benefit.
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