AI Platform Quality Engineer - CI/CD Gates & Evaluation

Intellias; Gijón; Presencial; EUR 55.000 - 90.000; Jornada completa

Descripción de la vacante

Intellia is seeking a Platform Engineer — CI/CD Gate & Online Evaluation to help establish automated quality controls for enterprise AI platforms and agent-based systems. You will integrate evaluation workflows into deployment pipelines and configure online quality monitoring.

Collaborate with platform, DevOps, AI engineering, and governance teams to ensure that AI agents meet quality standards before and after production deployment, while building visibility and reliability across the SDLC.

Formación

• 4+ years platform or DevOps engineering experience.
• Experience integrating CI/CD pipelines with quality gates.
• Design and implement CloudWatch dashboards and alarms.
• Experience with AWS AgentCore Evaluation API.
• Experience integrating evaluation in GitHub Actions.
• Configure online evaluation workflows and sampling strategies.

Responsabilidades

• Design and implement CI/CD quality gates using AWS AgentCore Evaluation.
• Integrate evaluation into GitHub Actions and deployment pipelines.
• Configure on-demand evaluation workflows for pre-release validation.
• Manage online evaluation capabilities and production sampling.
• Build CloudWatch dashboards to monitor evaluation scores over time.
• Configure alarms for quality degradation and performance regressions.
• Collaborate with AI engineers to define thresholds and criteria.
• Contribute to observability and governance for AI workloads.

Conocimientos

DevOps

CI/CD

CloudWatch

AWS AgentCore

GitHub Actions

Online evaluation

Dashboard design

Quality gates

Descripción del empleo

Intellia is seeking a Platform Engineer — CI/CD Gate & Online Evaluation to help establish automated quality controls for enterprise AI platforms and agent-based systems. You will integrate evaluation workflows into deployment pipelines and configure online quality monitoring.

Collaborate with platform, DevOps, AI engineering, and governance teams to ensure that AI agents meet quality standards before and after production deployment, while building visibility and reliability across the SDLC.