Machine Learning / MLOps Engineer

Salary: $94,000 - 134,000 per year Requirements:

• We are looking for proven experience as an ML Engineer, Data Engineer, or Software Engineer, with a strong emphasis on deploying, monitoring, and scaling machine learning systems in production.
• We value a pragmatic, proactive mindset that favors speed, reliability, and business impact over overly complex infrastructure.
• We need strong Python development skills, along with a solid understanding of software engineering practices, API design, and automated testing.
• We require extensive practical experience with Google Cloud Platform (GCP).
• We prefer hands-on experience with Vertex AI, including Pipelines, Endpoints, and Workbench.
• We need proficiency with containerization technologies such as Docker and with orchestration tools.
• We look for excellent communication skills and the ability to explain technical concepts to data scientists and operational needs to product leaders.
• We value experience with Infrastructure as Code tools such as Terraform.
• We prefer experience running containerized workloads on Google Kubernetes Engine (GKE).
• We welcome familiarity with real-time streaming platforms such as Apache Kafka or GCP Pub/Sub.

• We own the deployment of machine learning models into production and build scalable, low-latency prediction endpoints using GCP Vertex AI.
• We design, implement, and maintain CI/CD/CT pipelines for machine learning workflows using Vertex AI Pipelines, Cloud Build, and related GCP services.
• We set up automated monitoring and alerting solutions, such as Vertex AI Model Monitoring, to track data drift, model drift, and system performance in real time.
• We promote strong software engineering practices across the Data Science team, including unit testing, containerization, version control, and CI/CD automation.
• We work closely with the Data Science Team Leader, Junior Data Scientists, and the AgentOps Team Lead to speed up deployment cycles, remove operational blockers, and sustain high delivery velocity.

• AI
• API
• CI/CD
• Cloud
• Docker
• GCP
• Kafka
• Kubernetes
• Machine Learning
• Python
• Terraform

More:

We are collaborating with Bet365 to connect them with exceptional professionals for this role. We are building a newly established US Data team focused on operationalizing and scaling machine learning models from prototype to robust, production-ready systems. This is a hands-on role where we value automation, software engineering excellence, and MLOps, and you will act as the bridge between model development and dependable platform engineering. You will report to the Data Science Team Leader and work closely with the US AgentOps Team Lead and our UK technical excellence center.

last updated 40 week of 2026