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SeniorFull-timePythonJavaGCPKubernetesDockerTerraformMicroservicesMachine LearningData ScienceAgile
Salary Range
$132,600 - $179,400 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are seeking a Senior Machine Learning Engineer to join our data team and lead the creation of scalable AI/ML solutions. You’ll work in the Fraud Prevention Team, helping various business areas use data to automate processes, improve decisions, and fight financial crime. This position lets you work on impactful, real-time machine learning systems in a collaborative and agile setting.
Key Responsibilities
- →Lead and help deliver complex AI/ML projects focused on scalability, reliability, and quality.
- →Design and implement cloud-based ML systems (preferably on GCP).
- →Build and operate real-time ML and MLOps workflows in production.
- →Oversee the full lifecycle of ML solutions, ensuring they are robust, high-performing, and secure.
- →Mentor junior engineers and promote best practices.
- →Work with business and technical teams to turn requirements into valuable AI/ML solutions.
- →Identify and drive AI/ML use cases that improve processes and customer experience.
- →Develop, train, and evaluate machine learning models for business and risk challenges.
- →Manage technical risks and coordinate across teams.
- →Collaborate with cross-functional teams to deliver production-ready, maintainable systems.
Requirements
- ✓MSc in Computer Science, Data Science, Statistics, Mathematics, or similar—or equivalent experience.
- ✓5+ years of hands-on experience in machine learning and software development, with ML models in production.
- ✓Strong background in designing, building, and running real-time ML and MLOps workflows.
- ✓Deep knowledge of system architecture, performance, scalability, and security.
- ✓Proficient in Python and SQL; good understanding of data warehouse architecture.
- ✓Proven MLOps/DevOps skills, including CI/CD, testing, monitoring, and supporting ML systems in production.
- ✓Experience in data engineering, software engineering, and data science for effective teamwork.
- ✓Strong experience deploying ML models in cloud environments, preferably Google Cloud Platform (GCP).
- ✓Experience with Git, cloud platforms, and remote-first collaboration.
