Senior AI Infrastucture Engineer

 Posted 20 days ago
  
 Brazil
  
⭐ 5-10 years experience
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AI Summary

Design and deploy AI-driven platforms to automate enterprise infrastructure operations and improve service reliability. Focus on reducing operational overhead and optimizing cloud spending through intelligent automation and observability.

About Pyyne

Pyyne is a modern technology consultancy engineering the next generation of digital products and services.

At Pyyne, we believe in using technology to unlock business potential, create sustainable growth, and drive forward digital excellence. Our solutions range from advanced Software Engineering, Cloud, and Data & AI solutions.

Job Summary

Pyyne is seeking a Senior AI Infrastucture Engineer to work as a consultant out of our South American Office. This is a high-impact role for Pyyne as we expand our collaboration with a large financial services client..

A successful candidate will be responsible for designing, implementing, and operating AI-driven solutions that improve the efficiency, reliability, automation, and cost-effectiveness of enterprise infrastructure services. This role combines cloud engineering, infrastructure operations, automation, and AI technologies to transform traditional IT operations into proactive, intelligent, and self-healing environments.

Objective:

Leverage AI, automation, and modern infrastructure platforms to reduce operational overhead, optimize technology spending, improve service availability, and accelerate issue resolution across the enterprise infrastructure landscape.

Key Responsibilities

AI Platform & Integration

  • Design and deploy AI platforms that integrate with infrastructure tools, monitoring systems, CMDBs, ITSM platforms, cloud environments, and operational data sources.

  • Develop AI-powered workflows that automate routine operational tasks and decision-making processes.

  • Integrate large language models (LLMs), AI agents, and machine learning services into infrastructure operations.

Infrastructure Automation

  • Build AI-driven automation for incident response, remediation, patching, provisioning, and operational workflows.

  • Develop self-service capabilities that reduce manual support effort through intelligent assistants and virtual agents.

  • Automate repetitive engineering and operational tasks using Infrastructure-as-Code (IaC) and AI-assisted orchestration.

Operational Intelligence & Observability

  • Implement AI-powered monitoring, anomaly detection, predictive alerting, and root-cause analysis capabilities.

  • Create intelligent operational dashboards that provide actionable insights and recommendations.

  • Improve Mean Time to Detect (MTTD) and Mean Time to Resolve (MTTR) through AI-driven event correlation and automation.

Cloud & Cost Optimization (FinOps)

  • Develop AI solutions that continuously analyze infrastructure consumption and identify cost-saving opportunities.

  • Implement predictive capacity planning and resource optimization across cloud and on-premises environments.

  • Create automated recommendations for rightsizing, reservation management, workload placement, and infrastructure utilization.

Reliability & Performance Engineering

  • Ensure AI platforms and automation solutions operate reliably in production environments.

  • Design scalable, resilient architectures capable of supporting enterprise-wide adoption.

  • Implement observability, logging, monitoring, and governance controls for AI-enabled infrastructure services.

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