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Machine Learning Infrastructure Engineer (AWS) at SKF AI

Yoqneam Illit, North District, Israel -

Full Time

Start Date

Immediate

Expiry Date

30 Aug, 26

Salary

0.0

Posted On

01 Jun, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AWS, Python, Docker, Kubernetes, EKS, CI/CD, DevOps, System Design, Backend Development, MLOps, Terraform, CloudFormation

Industry

Description

We are looking for a Machine Learning Infrastructure Engineer (AWS) to join our AI Development Center in Yokneam. In this role, you will build and operate the production infrastructure powering our AI systems, enabling scalable and reliable AI solutions used by hundreds of industrial customers worldwide. You will work at the intersection of cloud infrastructure, backend engineering, and machine learning, collaborating closely with data scientists and product teams to bring models into real-world production environments. This is a hands-on engineering role with strong ownership, focused on designing and maintaining cloud-native systems on AWS that are robust, scalable, and production-grade. Requirements Strong hands-on experience working with AWS in production environments Experience building and operating cloud-based or distributed systems Proficiency in Python and/or backend development Experience with Docker and Kubernetes (EKS preferred) Familiarity with CI/CD pipelines and DevOps practices Understanding of system design, scalability, and reliability principles Strong problem-solving skills and ability to work in a collaborative environment Nice to Have: Experience with ML systems, pipelines, or model deployment Familiarity with MLOps practices Experience with infrastructure-as-code tools (Terraform, CloudFormation) Knowledge of monitoring and observability tools

Responsibilities

Build and operate production infrastructure to power scalable and reliable AI systems for industrial customers. Collaborate with data scientists and product teams to deploy machine learning models into real-world production environments.