DevOps Engineer - ML & Data Infrastructure

We’re looking for a DevOps Engineer to help design, build, and optimize the cloud infrastructure powering our machine learning operations. You’ll play a key role in scaling AI models from research to production — ensuring smooth deployments, real-time monitoring, and rock-solid reliability across our Google Cloud Platform (GCP) environment.

You’ll work hand-in-hand with data scientists, ML engineers, and other DevOps experts to automate workflows, enhance performance, and keep our AI systems running seamlessly for millions of players worldwide.

What You’ll Do

• Manage, configure, and automate cloud infrastructure using tools such as Terraform and Ansible.


• Implement CI/CD pipelines for ML models and data workflows, focusing on automation, versioning, rollback, and monitoring with tools like Vertex AI, Jenkins, and DataDog.


• Build and maintain scalable data and feature pipelines for both real-time and batch processing using BigQuery, BigTable, Dataflow, Composer, Pub/Sub, and Cloud Run.


• Set up infrastructure for model monitoring and observability — detecting drift, bias, and performance issues using Vertex AI Model Monitoring and custom dashboards.


• Optimize inference performance, improving latency and cost-efficiency of AI workloads.


• Ensure overall system reliability, scalability, and performance across the ML/Data platform.


• Define and implement infrastructure best practices for deployment, monitoring, logging, and security.


• Troubleshoot complex issues affecting ML/Data pipelines and production systems.


• Ensure compliance with data governance, security, and regulatory standards, especially for real-money gaming environments.
  
  

What We’re Looking For

• 3+ years of experience as a DevOps Engineer, ideally with a focus on ML and Data infrastructure.


• Strong hands-on experience with Google Cloud Platform (GCP) — especially BigQuery, Dataflow, Vertex AI, Cloud Run, and Pub/Sub.


• Proficiency with Terraform (and bonus points for Ansible).


• Solid grasp of containerization (Docker, Kubernetes) and orchestration platforms like GKE.


• Experience building and maintaining CI/CD pipelines, preferably with Jenkins.


• Strong understanding of monitoring and logging best practices for cloud and data systems.


• Scripting experience with Python, Groovy, or Shell.


• Familiarity with AI orchestration frameworks (LangGraph or LangChain) is a plus.


• Bonus points if you’ve worked in gaming, real-time fraud detection, or AI-driven personalization systems.