Production ML Engineer - MLOps & GCP Vertex AI

AgileGrid Solutions; United States; Remote; USD 120,000 - 190,000; Full time

Benefits offered by this job

Health insurance

Retirement plan

Generous PTO

Training & development

Flexible work options

Job summary

Bet365 seeks a pragmatic ML Engineer to join the US Data team, building automated infrastructure for the ML lifecycle and operationalizing models from data science into production-grade systems. You will focus on automation, maintainability, and scalable platform engineering, reporting to the Data Science Team Leader.

You will work closely with the AgentOps Team Lead and UK technical center to ensure seamless deployment, monitoring, and scaling of models in production environments, emphasizing

Qualifications

• Proven experience deploying ML systems in production.

Responsibilities

• Own the deployment of machine learning models to production with scalable predictors.
• Design and maintain CI/CD/CT pipelines for ML workflows using Vertex AI Pipelines and Cloud Build.
• Set up automated monitoring and alerting for data drift and model performance via Vertex AI Monitoring.
• Champion software engineering best practices within the data science team, including testing and versioning.
• Collaborate with data scientists and Ops leads to accelerate deployment cycles and maintain velocity.
• Continuously evaluate infrastructure and deployment strategies to improve reliability and scalability.

Skills

Python

API development

Automated testing

GCP

Vertex AI

Docker

GKE

Terraform

Kafka

Pub/Sub

Communication

Tools

Docker

Kubernetes (GKE)

Terraform

Vertex AI

Cloud Build

GCP

Apache Kafka

GCP Pub/Sub

Job description

Bet365 seeks a pragmatic ML Engineer to join the US Data team, building automated infrastructure for the ML lifecycle and operationalizing models from data science into production-grade systems. You will focus on automation, maintainability, and scalable platform engineering, reporting to the Data Science Team Leader.

You will work closely with the AgentOps Team Lead and UK technical center to ensure seamless deployment, monitoring, and scaling of models in production environments, emphasizing