Infrastructure & Reliability Engineer, Forward Deployed

About OryonTech

OryonTech develops Agent as a Service, a WhatsApp-first platform that enables businesses to manage customer interactions, orders, bookings, payments, and operational workflows.

Our infrastructure is GCP-native and Terraform-managed. Production reliability, secure access, recoverability, and cost control are part of the product because merchants rely on the platform for real business work.

Position overview

We are seeking an infrastructure and reliability engineer to own the systems that let OryonTech deploy safely, detect problems quickly, recover predictably, and scale without losing control of cost or security.

This is not a click-ops or ticket-only DevOps role. You will work directly with backend, Applied AI, frontend, product, and delivery colleagues, understand the operational requirement behind a release or customer deployment, shape the infrastructure solution, implement it as code, and verify the result in production.

You will own infrastructure as code, release automation, environments, service permissions, secrets, networking, observability, capacity, cost visibility, backup and recovery, and operational runbooks.

AI-assisted engineering (required)

You must actively use company-approved AI coding assistants and agentic development tools as part of your daily engineering workflow to build and improve production software efficiently. This is a core requirement, not an optional advantage, and applies to your own development process, not only to building AI product features.

• Use AI tools for research, planning, prototyping, implementation, refactoring, testing, debugging, documentation, and production diagnosis when appropriate to the task.
• Supply relevant codebase context, API contracts, constraints, failure cases, and acceptance criteria; iterate deliberately rather than accepting the first generated answer.
• Understand, review, test, and explain every change you ship. AI output does not replace engineering judgment, code review, security checks, or deployment approval.
• Protect credentials, personal information, customer data, and proprietary code. Use only approved tools and data-handling settings, keep access least-privileged, and never give AI tools uncontrolled production access.
• Build reusable prompts, instructions, tests, and automation that reduce repetitive work. Demonstrate efficiency through delivery time, reduced rework, and reliable outcomes, not lines of generated code; keep tool usage and cost proportionate.
• Use AI tools to accelerate Terraform and CI/CD changes, operational scripts, incident diagnosis, and runbooks; inspect plans and diffs and prove rollback and recovery before risky changes.

During technical screening, be ready to walk through an AI-assisted change, explain what you accepted or rejected, and show how you verified correctness. No additional document or exercise is required with your initial CV email.

Principal responsibilities

Cloud infrastructure and platform reliability

• Own and evolve GCP infrastructure using Terraform and version-controlled modules. Manual console changes should be exceptional, documented, and reconciled into code.
• Operate Cloud Run services, Cloud SQL PostgreSQL, Memorystore/Redis, VPC networking, Cloud NAT, Secret Manager, Cloud Storage, Pub/Sub, Cloud Scheduler, Artifact Registry, and related services.
• Maintain environment parity, service accounts, least-privilege IAM, secrets handling, private networking, and secure deployment patterns.
• Define capacity limits, scaling behavior, health checks, timeouts, and resource configuration appropriate to actual demand.
• Maintain cost attribution and visibility so infrastructure decisions can be evaluated against business value.

CI/CD and release engineering

• Own reliable build, test, image, and deployment pipelines from GitHub through GCP.
• Ensure releases are traceable, repeatable, and recoverable with clear rollback procedures.
• Reduce manual deployment steps and prevent configuration drift across development, staging, and production.
• Partner with engineers to improve deployment safety, migration sequencing, feature rollout, and environment-specific validation.
• Maintain release evidence and useful runbooks so another engineer can operate the system when needed.

Observability, incident response, and recovery

• Define useful logs, metrics, alerts, dashboards, and service objectives for critical workflows.
• Build alerts that are actionable and proportionate rather than noisy.
• Lead or coordinate diagnosis of infrastructure and deployment incidents, establish impact, restore service, and drive corrective action.
• Own backup configuration and prove restore procedures through scheduled recovery tests.
• Maintain incident and disaster-recovery runbooks and train a capable operational backup.
• Work with application engineers on correlation IDs, structured logging, provider health, queue behavior, and production diagnostics.

Security and operational control

• Maintain least-privilege access, secrets management, auditability, network controls, and secure service-to-service configuration.
• Review infrastructure changes for security, reliability, and cost consequences.
• Protect production from unreviewed changes, leaked credentials, public data exposure, and unsafe defaults.
• Support compliance and data-protection requirements with appropriate technical controls and evidence.

Forward-deployed engineering

• Participate in technical discovery when customer requirements affect deployment, integrations, security, data residency, capacity, or reliability.
• Convert repeated deployment and operational needs into reusable infrastructure modules and platform capabilities.
• Communicate risks and trade-offs in plain language and bring a recommended path rather than waiting for complete instructions.
• Use approved AI engineering tools where they improve investigation, automation, testing, documentation, or repetitive infrastructure work while validating all generated changes.

Required qualifications

• Demonstrated practical use of AI-assisted engineering tools to deliver tested software, with the ability to independently explain, debug, and maintain the resulting work.
• Strong professional experience operating production cloud infrastructure and CI/CD for customer-facing software.
• Hands-on Google Cloud Platform experience or deep equivalent experience on AWS/Azure with the ability to operate effectively in GCP.
• Strong Terraform/IaC skills and experience building reusable, reviewable infrastructure modules.
• Experience with containers, Docker, serverless/container platforms, networking, DNS, IAM, secrets, managed databases, and caches.
• Experience designing and operating CI/CD pipelines with safe deployment and rollback behavior.
• Practical monitoring, logging, alerting, incident-response, backup, and recovery experience.
• Strong understanding of least privilege, network isolation, secret handling, and production access controls.
• Ability to diagnose failures across infrastructure, application runtime, networking, permissions, deployment, and external services.
• Evidence of independent judgment, clear documentation, and production ownership.

Preferred experience

• Cloud Run, Cloud SQL, Memorystore, Pub/Sub, Cloud Scheduler, Artifact Registry, Cloud Armor, and Google Secret Manager.
• GitHub Actions or Cloud Build.
• PostgreSQL operations, connection management, backup/restore, and performance troubleshooting.
• Multi-tenant SaaS, payment systems, messaging platforms, or other reliability-sensitive products.
• FinOps, cost allocation, budgets, quotas, and capacity planning.
• Security/compliance programs, audit evidence, or ISO 27001 readiness.
• Previous platform engineering, SRE, forward-deployed, or startup infrastructure ownership.

Performance expectations

Success will be assessed against operational outcomes:

• Engineers can deploy safely without founder intervention or manual production procedures.
• Critical failures are detected quickly with actionable diagnostics.
• Rollback and recovery paths are tested and usable.
• Infrastructure, access, secrets, and environment configuration remain reproducible and auditable.
• Cost and capacity are visible and proportionate to actual usage.
• Recurring deployment and reliability needs become reusable platform capabilities.
• Another engineer can follow the documentation and operate essential systems when you are unavailable.

Specific targets will be established from the current platform baseline and the priorities agreed for the role.