Senior Backend Engineer - Platform and API

Job summary

We are building the trust layer for the agentic future. As AI agents move from simple chatbots to autonomous workloads with access to sensitive enterprise data, the industry lacks a standard for Identity, Authentication, and Governance. Our platform does not stop at application code. It also creates and manages the secure cloud infrastructure in which AI-agent systems are deployed and operated. That makes infrastructure design a core product capability, not a supporting function.

This role will lead the design and implementation of the multi-cloud platform that supports customer environments, secure deployment patterns, cloud networking, and production guardrails. It is a hands-on principal engineering role for someone who wants to build the underlying infrastructure that makes secure AI-agent deployment possible at scale. 

What you will do

• Lead the architecture for secure customer infrastructure across AWS, GCP, Azure, and Kubernetes environments.
• Define how customer environments are provisioned, configured, upgraded, and supported using infrastructure as code and repeatable deployment patterns.
• Drive technical decisions for cloud networking, cluster security, access control, secrets handling, observability, and operational guardrails.
• Build and review Terraform modules, cluster deployment workflows, platform automation, and release processes for multi-cloud environments.
• Partner with security, backend, and runtime teams to ensure infrastructure can support identity, policy enforcement, audit, and production operations from day one.
• Help establish the infrastructure standards that allow customers to adopt AI-agent systems with stronger security and operational confidence.
• Mentor engineers on infrastructure design, cloud-native security, Kubernetes operations, and engineering-owned production support.

What you will have

• 12+ years of software or platform engineering experience, with at least 5+ years in Kubernetes and public cloud infrastructure.
• Strong hands-on expertise in Kubernetes, Terraform, AWS, and at least one of GCP or Azure.
• Experience designing secure cloud environments, network boundaries, deployment workflows, and infrastructure automation.
• Strong understanding of container platforms, cluster security, ingress and egress controls, cloud IAM, secrets management, and observability.
• Experience supporting production systems and driving operational improvement without relying on a separate operations team.
• Ability to lead architecture while still reviewing implementation details and troubleshooting production issues when needed.

Nice to have

• Good to have: Experience with AI/ML managed services - AWS Bedrock, GCP Vertex AI, or Azure OpenAI Service - and familiarity with the infrastructure patterns they require (VPC endpoints, IAM, data residency, model routing). 

Benefits

• Generous time off policies
• Top shelf benefits
• Education, wellness and lifestyle support

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