Lead DevOps Engineer

Jobtailor; São Paulo; Presencial; BRL 180 000 - 320 000; Tempo integral

Resumo da oferta

Jobtailor seeks an experienced Infrastructure Engineer to lead the architecture and reliability of AI-powered systems at scale. You will own SLO/SLI design, observability, and resilient deployment patterns across cloud environments.

Responsibilities include mentoring engineers, enforcing standards, building secure distributed infra with Terraform and Kubernetes, and shaping the culture of reliability with a focus on drift, latency, and cost management.

Qualificações

• Extensive infra engineering experience combining DevOps and SRE at scale.
• Deep GCP expertise (AWS a strong plus) and relevant cloud certifications.
• Production experience with SRE fundamentals: SLO/SLI design, error budgets, toil reduction.
• Strong background in distributed systems failure modes and resilience patterns.
• Expert‑level infrastructure‑as‑code (Terraform), container orchestration (Kubernetes), and CI/CD.
• Hands‑on with modern observability stacks (OpenTelemetry, Sentry) and AI‑specific observability tooling.
• Experience with API management platforms, particularly Apigee and Cloud Run.
• Comfort working across Python, JavaScript, and Bash for infra tooling.
• Strong spoken and written communication in English with teams and stakeholders.

Responsabilidades

• Lead the architecture and maintenance of the infrastructure and reliability practices that keep AI‑powered systems performant, observable, and trustworthy under real production load, including redundancy, latency, and cost management.
• Help define SLOs/SLIs for AI‑powered services, including latency and quality SLOs for LLM inference paths, and build the error‑budget discipline that lets product teams ship fast without breaking trust.
• Design scalable, secure infrastructure for distributed AI services, event‑driven workloads, and multi‑LLM‑provider integrations.
• Build metrics, tracing, and alerting that surface not just 'is it up' but 'is it behaving correctly' for LLM‑powered features (drift, regression, hallucination rates, tool‑call failures).
• Define and enforce PRR‑style standards across teams launching new AI products and features.
• Mentor engineers, drive architecture reviews, and shape the broader engineering culture around reliability.

Conhecimentos

DevOps

SRE

GCP

Terraform

Kubernetes

CI/CD

Observability

AI observability

Python

JavaScript

Bash

English fluency

APIs

Formação académica

Bachelor's degree in Computer Science

Ferramentas

Kubernetes

Terraform

Sentry

OpenTelemetry

Apigee

Cloud Run

Descrição da oferta de emprego

Responsibilities

• Lead the architecture and maintenance of the infrastructure and reliability practices that keep AI‑powered systems performant, observable, and trustworthy under real production load, including redundancy, latency, and cost management.
• Help define SLOs/SLIs for AI‑powered services, including latency and quality SLOs for LLM inference paths, and build the error‑budget discipline that lets product teams ship fast without breaking trust.
• Design scalable, secure infrastructure for distributed AI services, event‑driven workloads, and multi‑LLM‑provider integrations.
• Build metrics, tracing, and alerting that surface not just "is it up" but "is it behaving correctly" for LLM‑powered features (drift, regression, hallucination rates, tool‑call failures).
• Define and enforce PRR‑style standards across teams launching new AI products and features.
• Mentor engineers, drive architecture reviews, and shape the broader engineering culture around reliability.

Requirements

• Significant infrastructure engineering experience combining DevOps and SRE disciplines at scale.
• Deep GCP expertise (AWS a strong plus); relevant cloud certifications welcome.
• Production experience with SRE fundamentals: SLO/SLI design, error budgets, toil reduction, blameless incident review.
• Strong background in distributed systems failure modes and resilience patterns.
• Expert‑level infrastructure‑as‑code (Terraform), container orchestration (Kubernetes), and CI/CD.
• Hands‑on with modern observability stacks (i.e., OpenTelemetry, Sentry) and AI‑specific observability tooling (Arize, LangSmith, Braintrust, or similar).
• Experience with API management platforms, particularly Apigee and Cloud Run.
• Comfort working across Python, Javascript, and Bash for infra tooling.
• Strong spoken and written communication in English with teams and stakeholders.