Lead DevOps Engineer - AI-Driven Platform & Automation

Amaris Consulting; Lima Metropolitana; Presencial; PEN 203.804 - 305.706; Jornada completa

Descripción de la vacante

Amaris Consulting in Lima, Peru is seeking a Lead DevOps Engineer to evolve our platform into an AI-enabled delivery ecosystem, focusing on platform engineering, AI-assisted development, and intelligent automation of software delivery and operations.

You will drive CI/CD improvements, integrate AI copilots and automation, and enable self-healing pipelines across data and AI workloads, collaborating with data engineers and product teams to ensure scalable, reliable deployments with strong

Formación

• Experience with modern DevOps practices and platform engineering.
• Proficiency in CI/CD design and automation.
• Experience embedding AI-driven practices in DevOps.
• Strong understanding of cloud-native architectures (Azure).
• Familiarity with data platforms and AI workloads.

Responsabilidades

• Define and implement a modern DevOps strategy aligned with data and AI goals.
• Develop roadmaps for AI-assisted development, testing, and operations.
• Drive intelligent, self-service platform capabilities and self-healing pipelines.
• Integrate AI copilots and automation into development and deployment workflows.
• Ensure governance, security, and reliability across pipelines and environments.
• Collaborate with data engineers and product teams to deploy AI-enabled data products.

Conocimientos

CI/CD orchestration

Infrastructure as code

Scripting

Azure cloud

Data platforms & AI workloads

Observability & SRE

Herramientas

Azure DevOps

GitHub Actions

Bicep

ARM templates

Airflow

Databricks

AIOps tools

Descripción del empleo

Amaris Consulting in Lima, Peru is seeking a Lead DevOps Engineer to evolve our platform into an AI-enabled delivery ecosystem, focusing on platform engineering, AI-assisted development, and intelligent automation of software delivery and operations.

You will drive CI/CD improvements, integrate AI copilots and automation, and enable self-healing pipelines across data and AI workloads, collaborating with data engineers and product teams to ensure scalable, reliable deployments with strong