Machine Learning Engineer

bet365; Denver (CO); On-site; USD 120,000 - 140,000; Full time

Job summary

bet365 is hiring an ML Engineer/Data Engineer to operationalize and scale models from prototype to production-grade systems. You will build reliable, automated endpoints and align with the Data Science Team Leader and US AgentOps Team Lead.

The role emphasizes rapid, maintainable software, MLOps, CI/CD for ML workflows, and real-time monitoring of data/model drift. You will collaborate with data scientists and platform engineers to accelerate deployment velocity on GCP Vertex AI and Kubernetes.

Qualifications

• Proven experience deploying ML systems to production.
• Strong Python programming with API development and testing.
• Hands-on experience with GCP and Vertex AI.
• Familiarity with containerization and Kubernetes.
• Experience with IaC tools like Terraform.
• Excellent written and verbal communication.

Responsibilities

• Owning the deployment of ML models to production and maintaining scalable, low-latency endpoints.
• Designing and maintaining CI/CD/CT pipelines for ML workflows using Vertex AI Pipelines and related GCP tools.
• Setting up automated monitoring and alerting for data drift, model drift, and system performance in real-time.
• Championing best practices for software engineering within the Data Science team, including testing and version control.
• Collaborating with the Data Science Team Leader and AgentOps Team Lead to accelerate deployment cycles.

Skills

Python programming

Production ML deployment

Communication skills

Tools

GCP

Vertex AI

Docker

Kubernetes (GKE)

Terraform

CI/CD tooling

Kafka

Pub/Sub

Job description

We’re one of the world’s leading online gambling companies, revolutionising the industry since 2000. Founded by Denise Coates CBE, we now employ over 10,000 people and serve over 120 million customers in 26 languages.

We empower our employees to push boundaries and explore new ideas, cultivating a culture that celebrates and rewards creativity. This offers employees a wealth of growth opportunities, giving them the opportunity to make a real impact in the world of online gambling. As a forward-thinking company, we’re breaking new ground in software innovation too, redefining what’s possible for our global worldwide.

Our focus on In-Play betting has solidified our market-leading position, featuring more than 1.38 million In-Play sporting events a year. With over 750 concurrent sporting fixtures at peak and more live sports streamed than anyone else in Europe (750,000), we handle over 6 million HTTP requests daily and process more than 1.5 million bets per hour at peak.

Job Description

Your primary mission is to operationalize and scale the models developed by our data science team, taking them from prototype to robust, production-grade systems with high velocity.

You’ll focus on building reliable, automated and maintainable systems, keeping solutions pragmatic rather than over-engineered. You will also be passionate about automation, software engineering excellence, and MLOps.

You will report to the Data Science Team Leader and work in close alignment with the US AgentOps Team Lead (responsible for agentic and model orchestration platforms) and our UK technical excellence center. You will act as the bridge between model development and reliable platform engineering.

The listed salary for this position is $120,000 – $140,000 annually.

Qualifications

• Proven experience as an ML Engineer, Data Engineer, or Software Engineer with a clear focus on deploying, monitoring, and scaling machine learning systems in production.
• A pragmatic, proactive approach to system design, prioritizing speed, reliability, and business value over complex, theoretical infrastructure.
• Strong Python programming skills, with a solid grasp of software engineering patterns, API development, and automated testing frameworks.
• Extensive hands-on experience with Google Cloud Platform (GCP).
• Practical experience with Vertex AI (specifically Vertex AI Pipelines, Endpoints, and Workbench).
• Proficiency with containerization (Docker) and container orchestration tools.
• Excellent communication skills, with the ability to translate software engineering concepts for data scientists and operational requirements for product leads.
• Experience utilizing Infrastructure as Code (IaC) tools such as Terraform.
• Experience running containerized workloads on Google Kubernetes Engine (GKE).
• Familiarity with real-time streaming tools like Apache Kafka or GCP Pub/Sub.

Additional Information

• Owning the deployment of machine learning models to production. Build and maintain scalable, low-latency prediction endpoints using GCP Vertex AI.
• Designing, implementing, and maintaining CI/CD/CT (Continuous Integration, Continuous Delivery, Continuous Training) pipelines for machine learning workflows using Vertex AI Pipelines, Cloud Build, and related GCP tools.
• Setting up automated monitoring and alerting frameworks (e.g., Vertex AI Model Monitoring) to track data drift, model drift, and system performance in real-time.
• Championing best practices for software engineering within the Data Science team, including robust unit testing, containerization, version control, and CI/CD automation.
• Working closely with the Data Science Team Leader, Junior Data Scientists, and the AgentOps Team Lead to accelerate deployment cycles, remove operational bottlenecks, and maintain high deployment velocity.

At bet365, we're committed to creating an environment where everyone feels welcome, respected and valued. Where all individuals can grow and develop, regardless of their background. We're Never Ordinary, and we're always striving to be better. If you need any adjustments or accommodations to the recruitment process, at either application or interview, please don’t hesitate to reach out.