AIOps Engineer: AWS & Python for AI Data Platform

ERNI Spain; Madrid; Ibrido; EUR 65.000 - 90.000; Tempo pieno

Vantaggi offerti da questo lavoro

Certification reimbursements up to €1,

Private health insurance

Sick leave coverage

Emotional, legal and family support

Gym discounts

Flexible remuneration

Referral bonuses: up to €6,000 per new

23 days vacation

Language courses: English, Spanish, 0

Descrizione del lavoro

ERNI is seeking a senior cloud platform engineer to design, deploy and evolve AWS infrastructure for AI and data environments. You will support AI workloads, improve observability and enable RAG/AI agent integrations while driving cost efficiency, security and reliability.

The role emphasizes mentoring, autonomy and cross-team collaboration, with a hybrid work model and regular development planning to align short-, medium-, and long-term goals.

Competenze

• Strong hands-on experience with AWS environments and cloud infrastructure.
• Solid experience with Python and Infrastructure as Code using Terraform or AWS CDK.
• Experience with MLOps, AIOps or Data Platform environments including monitoring, troubleshooting and observability.
• Knowledge of AI integrations including RAG, LLMs, Claude / Anthropic API or MCP.
• English level B2 or higher and experience working with Jira, Confluence and Bitbucket.

Mansioni

• Design, deploy and evolve AWS infrastructure for AI and data environments.
• Support AI and Data teams by troubleshooting pipelines and execution environments.
• Enable RAG and AI agent integrations and improve observability through logs, metrics and monitoring.
• Develop internal Python tools, libraries and reusable frameworks.

Conoscenze

AWS

Python

Terraform

MLOps

AIOps

Data Platform

Observability

RAG integrations

LLMs

Claude/Anthropic API

MCP

Jira

Confluence

Bitbucket

English (B2+)

Strumenti

Jira

Confluence

Bitbucket

Descrizione del lavoro

ERNI is seeking a senior cloud platform engineer to design, deploy and evolve AWS infrastructure for AI and data environments. You will support AI workloads, improve observability and enable RAG/AI agent integrations while driving cost efficiency, security and reliability.

The role emphasizes mentoring, autonomy and cross-team collaboration, with a hybrid work model and regular development planning to align short-, medium-, and long-term goals.