Staff DevOps Engineer for AI Platform & Kubernetes

Ansys; Málaga; Presencial; EUR 55.000 - 90.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health benefits

Wellness program

Financial benefits

Descripción de la vacante

Ansys is seeking an experienced Platform Engineer to scale and operate a SaaS-based SimAI platform. You will design and maintain Kubernetes-led infrastructure, automate deployments, and drive observability to support AI model training and inference at scale.

You will collaborate with a distributed team across time zones, mentoring peers and implementing RFC-driven improvements while ensuring reliability and performance.

Formación

• 3 years' experience with Kubernetes-based platforms.
• Advanced troubleshooting skills.
• Experience with distributed systems (Kubernetes, Pulumi, NATS, Redis).
• Scripting with Bash; programming in Python or TypeScript.
• Ability to own problems end-to-end, from definition through deployment and monitoring.

Responsabilidades

• Perform complex development activities in cloud deployment and scale across distributed systems.
• Use best practices and evangelize through RFCs and mentoring.
• Help scale release processes, development environments, and CI/CD pipelines.
• Root-cause investigation, automated release testing, production incident solving.
• Design, deploy, and maintain platform infrastructure using Kubernetes, Pulumi, and Terraform.
• Scale GPU and CPU compute resources to support AI model training and inference workloads.
• Build monitoring and observability tooling with Prometheus and Grafana.
• Mentor engineers through RFCs, documentation, and pairing sessions.

Conocimientos

Kubernetes

Distributed systems

Python/TypeScript

Bash scripting

Troubleshooting

Educación

Software development certification

Herramientas

Pulumi

NATS

Redis

Prometheus

Grafana

Terraform

Descripción del empleo

Ansys is seeking an experienced Platform Engineer to scale and operate a SaaS-based SimAI platform. You will design and maintain Kubernetes-led infrastructure, automate deployments, and drive observability to support AI model training and inference at scale.

You will collaborate with a distributed team across time zones, mentoring peers and implementing RFC-driven improvements while ensuring reliability and performance.