Senior Platform Engineer

Senior Platform Engineer

Who We Are

MOXFIVE is building technologies that leverage AI to streamline response, recovery, and resilience from cyber attacks in enterprises. We are looking for a Senior Platform Engineer to join our engineering team, where your work will directly shape the reliability, security, and deployability of our platform.
The MOXFIVE founding team brings over 30 years of combined cybersecurity experience from leading companies such as CrowdStrike, Mandiant, Palo Alto Networks, Crypsis, Illumio, and Expel. Today, we are focused on seizing an opportunity with the potential to disrupt the breach response market.

Who You Are

As a senior platform engineer you like building the foundations that help product teams ship quickly, safely, and with confidence. You are comfortable owning cloud infrastructure, Kubernetes workloads, CI/CD pipelines, secrets management, deployment controls, observability, and production runbooks.
The practical side of platform engineering is where you do some of your best work: making releases predictable, failures easier to diagnose, local development smoother, and production access secure without creating unnecessary friction.
Security and reliability matter deeply to you, but you bring judgment rather than rigidity. You know when to automate, when to document, when to add guardrails, and when to keep a path manual because the blast radius deserves care.
High-velocity teams energize you. You are comfortable moving quickly, making sound decisions with imperfect information, and strengthening systems as the product and engineering organization grow..

The Impact You Will Have

At MOXFIVE, reliability is not just an engineering concern. It is part of the trust customers place in us during high-pressure cyber incidents. When decisions are being made quickly, our systems need to be secure, dependable, understandable, and ready.
As our Senior Platform Engineer, your work will strengthen the path from code to production: improving delivery, hardening infrastructure, clarifying operational signals, and reducing the friction that slows teams down when speed matters. You will help engineers release with confidence and give response teams better visibility into the systems they depend on.
This is a high-leverage role for someone who combines technical depth, security judgment, and a bias toward execution. The platforms, controls, and practices you build will shape how MOXFIVE develops, releases, observes, and protects software used by enterprises responding to cyber incidents.

What You Will Do

• Own and improve the platform foundation that helps a high-velocity engineering team ship safely across cloud infrastructure, Kubernetes, IaC, secrets, networking, access controls, CI/CD, observability, and production guardrails.

• Build internal tooling for an AI-enabled engineering workflow, including automation, repo and CI feedback loops, agent-ready development environments, and safeguards that let engineers move quickly without weakening production discipline.

• Strengthen operational readiness through better logging, metrics, tracing, alerting, runbooks, and incident follow-up.

• Harden production access with least-privilege IAM, secure secret management, auditability, and controlled break-glass paths.

• Set pragmatic platform standards that help a small team move quickly today while avoiding infrastructure, reliability, and security debt tomorrow.

What You Will Bring

• 5+ years of experience in platform engineering, DevOps, SRE, infrastructure engineering, or backend-adjacent cloud operations.

• A track record of owning production systems where reliability, security, and developer velocity all matter.

• Hands-on experience with cloud infrastructure, Kubernetes, infrastructure-as-code, CI/CD, secrets management, access controls, and observability.

• Experience building internal developer tooling, platform automation, or AI-assisted development workflows.

• Comfort designing safe release processes with deployment gates, smoke tests, rollback paths, and clear ownership.

• Practical experience supporting relational databases and production data changes.

• A security-minded approach to infrastructure, including least privilege, auditability, secret handling, and controlled production access.

• Clear written communication for runbooks, deployment notes, incident follow-ups, and engineering decisions.

Technologies You May Work With

We care more about strong platform judgment than exact tool overlap. Our environment includes technologies in these categories:

• Cloud and Kubernetes: AWS or comparable cloud platforms, managed Kubernetes, container registries, IAM, private networking, and secure cluster access.

• Infrastructure: Terraform, OpenTofu, Terragrunt, or similar infrastructure-as-code and environment orchestration patterns.

• CI/CD: GitHub Actions or similar CI/CD systems, protected environments, federated identity, deploy gates, image pipelines, and smoke-test automation.

• Runtime Operations: API services, worker services, durable workflow/orchestration systems, event streaming, and relational databases.

• Security and Access: Least-privilege IAM, service tokens, secret rotation, zero-trust access patterns, production approval gates, and audit-friendly operational controls.

• Observability: APM, logs, metrics, tracing, alerting, Kubernetes visibility, and cloud integrations using tools such as Datadog, Honeycomb, Grafana, New Relic, or similar.

• Developer Experience: Docker, local Kubernetes, kubectl, task runners, local service scripts, and frontend build/deploy workflows.

Nice to Have

• Familiarity with agent harness design, agent sandboxing, including tool access, environment setup, state management, permissions, and production guardrails.

• Experience managing production model inference across hosted providers such as Together AI or Fireworks.ai, GPU platforms such as RunPod or Lambda Cloud, Modal, or similar, or self-hosted serving stacks, including the tradeoffs between hosted APIs, dedicated deployments, serverless GPUs, and self-hosted inference stacks.