MLOps Engineer (Data & AI, Data Operations Team)

Join our Data Operations Team as an ML Ops Engineer and play a key role in building a cutting-edge ML Platform on Google Cloud from the ground up—shaping the future of scalable, automated machine learning across the organization.

What you’ll have to do:

Managing and optimizing ML workflows in Google Cloud Platform, Cloud Functions, and BigQuery ML; integrating CI/CD pipelines for model training and deployment; monitoring and automating model performance and retraining in Kubernetes-based environments;

Define and manage cloud infrastructure using Infrastructure as Code (IaC) tools such as Terraform;

Configuring and tuning a variety of services in order to increase system stability, performance and security;

Capacity planning and performance tuning for high traffic events such as Black Friday;

Adding observability and alerting to data pipelines and services;

Testing new technologies and industry trends and assessing their fitness for our needs;

Sharing technical expertise and helping data engineers and data scientists implement best practices that maximize performance and security;

Identifying repetitive issues or tasks and focus on automation and implementation of permanent solutions.

What makes you a good fit:

Have very good knowledge of Google Cloud Platform, Linux environments, containers and container orchestration solutions (Kubernetes);

Have experience with at least one programming language (Python, Java, Ruby, PHP, etc), Shell scripting and regular expressions;

Have solid understanding of best practices around software development, release engineering and CI/CD;

Have good understanding of networking, operating systems, automation, distributed computing, high availability and load balancing concepts.

What makes you stand out: