Security & Platform Engineer

Job Description

About InfrasAI

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InfrasAI (formerly iLife Technologies) is a high-growth fintech and insurtech startup backed by top VCs, the same investors behind Uber, Netflix, Slack, and Ring.

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InfrasAI helps financial services and insurance companies use AI Agents to build and QA complex workflow mappings at the speed of thought, with a streamlined human-in-the-loop process to ensure enterprise-grade accuracy and compliance.

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About the Role

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InfrasAI’s platform, GitOps-driven Kubernetes spanning multiple AWS accounts and tenants, has been designed and operated by a small team. We’re now hiring an additional DevOps engineer to own and strengthen security across the entire platform.

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This is not a junior backfill or a ticket-taker role . This person will be a true peer on platform, infrastructure, architecture, and security, owning major workstreams end to end and helping set technical direction. We're a small team, so this comes with real autonomy and real impact. The right person can take an ambiguous problem and own it without a playbook handed to them.

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Quality of the hire is our number one priority. We would rather wait for the right person than fill the seat with the wrong one.

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What You'll Own

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• GitOps deployment architecture (ArgoCD, Argo Workflows, Helm) for a multi-tenant product — codifying it, hardening it, and evolving it as we grow.
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• Observability and alerting: turning this into something we genuinely trust, where alerts mean something and we hear about problems before customers do. (Grafana, Prometheus, Loki.)
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• Reliability and resilience: backups, disaster recovery, high availability, and the durability work that keeps us standing when something fails.
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• Solution and systems architecture: shared ownership of how our systems are designed across the stack, not just how they run.
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• Security — a security-first eye across the platform, covering infrastructure hardening, secrets management, least-privilege access, and keeping our systems and tenants safe.
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What We're Looking For

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• Strong solution and systems architecture depth. You can design whole systems and reason about tradeoffs across the entire stack, not just operate individual components.
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• Deep AWS expertise at a solutions-architect level, ideally across multiple accounts.
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• Strong security experience and a natural eye for it. You think about attack surface, least privilege, secrets handling, and tenant isolation by default, not as an afterthought.
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• Deep, hands-on experience running production Kubernetes (EKS a plus) and infrastructure as code (Terraform).
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• You've owned a production platform, not just contributed to one. You've been the person on the hook when it broke.
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• Real CI/CD, GitOps, and observability experience.
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• Hands-on experience building and leveraging AI systems into real production systems. You've started working AI into real systems, not just experimenting on the side. As an AI company in a fast-moving space, this matters to us.
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• You think in failure modes. You design for the bad day, not just the happy path.
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• You can operate independently, make good calls under ambiguity, and own the outcome.
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• You make the people around you sharper, and you collaborate openly to pressure-test ideas and arrive at the most robust solution rather than the first one. Someone worth thinking out loud with.
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Nice to Have

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• Production experience with Go, Python, Java, Typescript.
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• Multi-tenant SaaS infrastructure experience.
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• Experience as an early infrastructure hire in a growing company.
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Experience

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• 5+ years of hands-on platform or infrastructure engineering experience in production environments.
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• 3+ years of AWS experience at an architecture and design decision level, ideally across multiple accounts.
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• 3+ years of production Kubernetes ownership. You've operated the cluster, not just deployed to one someone else ran.
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• 3+ years of Terraform or equivalent infrastructure as code ownership in production.
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• Proven production platform ownership. You've been on-call, you've been on the hook when it broke, and you've handled it.
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• Hands-on ownership with AI systems in real production or commercial environments. Academic, hackathon, or side-project experience alone is not sufficient.
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Compensation

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Competitive base salary + equity + outcome-based performance bonus.