Senior Site Reliability Engineer Aliz · Jakarta · Site Reliability Pay not stated

Devopsroles; Jakarta Pusat; On-site; IDR 900,000,000 - 1,400,000,000; Full time

Job summary

Aliz in Jakarta is seeking a Senior Site Reliability Engineer to lead production engineering efforts and maintain reliable systems in a fast-moving AI-enabled environment.

You will build checks, configure SLOs/SLIs, automate incident response, and advance observability while keeping human review and safeguards at the core. This role collaborates with distributed teams and requires strong English communication.

Qualifications

• Six+ years in SRE, production engineering or infra-heavy backend roles.
• Hands-on GCP experience with Cloud Run, networking and IAM.
• Fluency with Terraform, CI/CD and release safeguards; can build tooling in Go/Python.
• Able to lead incident investigations and drive root-cause fixes.
• Experience using AI coding agents and reviewing their outputs.

Responsibilities

• Build checks, staged releases and recovery paths to deliver AI-assisted code safely.
• Set SLOs/SLIs and error budgets to balance speed and stability.
• Create agent workflows for alert triage, incident investigation and reporting with verification steps.
• Improve logs, metrics, traces and alerts; convert findings into runbooks and tests.
• Maintain Terraform and tooling; improve GCP networking and capacity management.
• Develop reliability tooling in Go/Python and review agent outputs.

Skills

SRE experience

GCP Cloud Run

Terraform

CI/CD

Go/Python

Incident investigations

AI agents

Tools

Terraform

Job description

Senior Site Reliability role at Aliz. Jakarta · full-time.

devopsroles summary, based on the employer's posting.

What you'll do

• Build checks, staged releases and recovery paths so AI-assisted code changes reach production safely
• Set SLOs, SLIs and error budgets, then use them to balance delivery pace with system stability
• Create agent workflows for alert triage, incident investigation, maintenance and reporting, with verification and approval steps
• Improve logs, metrics, traces and alerts, and turn incident findings into runbooks, tests and safeguards
• Maintain Terraform and delivery tooling while improving GCP networking, access controls and capacity management

What you bring

• At least six years in SRE, production engineering or infrastructure-focused backend work, with direct responsibility for live systems
• Strong hands-on GCP skills, including Cloud Run, networking and IAM; cloud experience is assessed in interviews
• You work fluently with Terraform, CI/CD and release safeguards, and can build operational tools in Go, Python or a comparable language
• You can lead incident investigations, find root causes and drive changes that prevent repeat failures
• You use coding agents in practice and can review, test and validate their output

Who this fits

This senior role suits an engineer who wants to make AI-assisted development faster without giving up verification, human judgment or production safeguards. You’ll collaborate asynchronously with a distributed team and need strong written English.

From the employer

The Role

Help Rabbit build and ship faster with AI — safely, securely and reliably.

Rabbit runs automated cost optimization across enterprise Google Cloud environments. When we change a customer's BigQuery reservations or rightsize their GKE clusters, those changes need to be correct and dependable. Reliability is central to the trust customers place in our product.

Our foundation is already in place: logging, alerting, automated deployment and Terraform-managed infrastructure. Your mission is to evolve that foundation for an AI-accelerated engineering team: turn faster implementation into faster, dependable delivery through automated validation, safe releases and rapid feedback.

You'll apply proven SRE practices — SLOs, observability, incident response and deployment safety — to AI-assisted development and agent-driven workflows. The goal is to increase how quickly the team can deliver verified improvements, while controlling production risk and reducing manual operational work.

What You'll Do

• Make AI-assisted delivery faster and safer. Build automated validation, progressive rollout and recovery mechanisms that let engineers and agents move quickly with clear checks before and after changes reach production.
• Make reliability measurable. Define and operationalize SLOs, SLIs and error budgets, and use them to guide practical decisions about delivery speed, stability and reliability work.
• Automate workflows with AI agents. Identify repetitive operational work and build reusable agent-driven workflows for alert triage, incident investigation, routine maintenance and reporting. Add verification and human approval where needed, and measure the reduction in manual effort.
• Improve observability and feedback. Evolve logging, metrics, tracing and alerting so failures are detected early and changes can be traced, investigated and verified.
• Turn incidents into lasting improvements. Improve runbooks, investigation and blameless postmortems, and translate recurring problems into tests, safeguards and automation.
• Keep infrastructure reproducible. Extend our Terraform and delivery tooling so environments remain consistent and changes stay reviewable as the platform grows.
• Improve GCP reliability and efficiency. Strengthen our cloud infrastructure, networking, access controls and capacity management, balancing performance, reliability and cost.
• Use AI to accelerate reliability engineering itself. Build maintainable tooling in Go, Python or a comparable language, and use agents to accelerate investigation, implementation, testing and documentation while verifying their outputs.

How We Work — AI-First, Agentic by Default

AI-assisted engineering is an expectation of this role, not an optional experiment. Claude Code, Cursor and agent-driven workflows are part of how we work, including infrastructure and reliability engineering.

We want someone who actively looks for ways to increase engineering speed with AI and makes those improvements safe to repeat. That means shorter feedback loops, automated checks, traceable changes and recovery paths — not simply generating more code.

You remain accountable for engineering judgment: what to automate, how to verify it, when human approval is needed and when a change should be stopped or rolled back. Success means faster delivery of reliable improvements, less repetitive work and a platform the team can trust.

What You’ll Bring

Must-have

• 6+ years in SRE, production engineering or infrastructure-heavy backend roles, with hands-on ownership of production systems.
• Strong production GCP experience, including Cloud Run, networking and IAM. Hands-on Google Cloud experience is required and will be assessed during the interview process.
• Infrastructure-as-code fluency with Terraform, plus solid experience in CI/CD and deployment safety.
• Strong observability and troubleshooting skills: you can make systems debuggable, identify root causes and verify that a fix works.
• Coding ability in Go, Python or a comparable language, with experience building maintainable operational tooling.
• Experience leading production incident investigation and driving follow-up improvements that prevent recurrence.
• Practical fluency with AI coding agents and the ability to critically review, test and validate their work. You are motivated to make AI-assisted engineering faster and more dependable.
• Strong written English and the discipline to collaborate asynchronously with a distributed team.

Nice to have

• Kubernetes / GKE experience, including deploying, operating, debugging and scaling containerized services.
• Hands-on Datadog experience, including dashboards, monitors, logs, APM and distributed tracing.
• Experience improving delivery speed and safety through progressive delivery, policy-as-code and automated rollback.
• GCP cost management or FinOps experience.
• Security experience is a plus.

Why This Role

• Shape how an AI-first engineering team scales. Build the reliability practices and automation that let Rabbit turn faster development into dependable customer outcomes.
• Work on systems with real customer impact. Rabbit operates inside enterprise GCP environments, where reliability and security directly affect customer trust.
• Own meaningful improvements. Work in a small team with short decision paths and end-to-end ownership, supported by appropriate review and production safeguards.
• Use AI as an engineering multiplier. Apply agentic tools to infrastructure, delivery and operations, and help define how we measure and improve their impact.

About Aliz

Aliz was founded by a group of driven software engineers with one vision in mind: Build a human-centered company to solve exciting business challenges with cutting-edge technologies all around the globe.

Our team includes engineers, industry experts, and digital professionals who ensure our clients are ready to face the challenges of the ever-changing digital world.

Aliz is a proud Google Cloud Partner with specializations in Infrastructure, Data Analytics, Cloud Migration, and Machine Learning. We deliver data analytics, machine learning, infrastructure, and application development solutions, off the shelf, or custom-built on GCP using an agile, holistic approach. Recently we've launched our very own cloud cost optimization tool Rabbit .

Life has been pretty exciting at Aliz recently ( here's proof ). After we became Google Cloud’s Breakthrough Partner of the Year in APAC (2019), we built a team in Singapore and Berlin, opened an engineering hub in Jakarta, launched a business in Italy and Qatar, and started to support IT education.

You couldn’t pick a better time to join this world-class team of cloud experts.