Middle Infrastructure Engineer (ML/AI)

Workato; Porto, Lisboa; Presencial; EUR 45 000 - 65 000; Tempo integral

Resumo da oferta

A tech company in Porto is seeking a Middle Infrastructure Engineer to deploy and maintain services for its ML/AI team. The role requires 3-5 years of engineering experience, strong Kubernetes expertise, and familiarity with hosting AI models. You will collaborate with a talented team, influencing platform modernization and architecture decisions. The ideal candidate should be open to communication in English.

Qualificações

• 3-5 years of professional experience in hands-on engineering roles.
• 6+ months of experience with hosting AI models using ML flow.
• Experience with vector databases like Qdrant.

Responsabilidades

• Deploy, scale, and maintain services for the ML/AI team.
• Work with ML Engineers and Data Scientists.
• Make infrastructure architecture decisions.

Conhecimentos

Kubernetes expertise

Experience with ML Ops

DevOps/SRE experience

Hands-on engineering roles

Formação académica

BS/MS degree in Computer Science or Engineering

Ferramentas

AWS SageMaker

Azure AI

Kubernetes

Descrição da oferta de emprego

Responsibilities

What does our ML/AI team do? The team supported a Generative AI solution for automation and integration.

Our Team consists of international Sr-level professionals from all over the globe, experienced in building high-performing, scalable, enterprise-grade applications. A unique team of multinational professionals with integration, cloud, and consumer experience.

We are currently seeking a promising Middle Infrastructure Engineer with experience in the AI domain.

As a Middle Infrastructure Engineer, you will be responsible for deploying, scaling, and maintaining services for the ML/AI team. You will closely work with ML Engineers and Data Scientists as part of a small, flexible team and will have a direct impact on the modernization and maturation of the platform, including infrastructure architecture decisions.

Requirements

Qualifications / Experience / Technical Skills

BS/MS degree in Computer Science, Engineering or a related subject (or equivalent experience)

3-5 years professional experience in hands-on engineering roles (DevOps/SRE)

6+ months of experience with hosting AI models (ML flow, AWS SageMaker, Azure AI, Kubernetes) is preferred

6+ months of experience with ML Ops (ML flow, vector databases (Qdrant), Dagster) is preferred

Kubernetes Expertise: Strong experience managing Kubernetes clusters and workloads, specifically using EKS (Elastic Kubernetes Service)

Soft Skills / Personal Characteristics

Readiness to communicate in English with your colleagues