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 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.

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.

Tech Stack

• 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.

Compensation Range

$180K - $220K