Role overview
- 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.
- 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.
Preferred qualifications
- Experience with containerized, microservices-based architectures using Docker and Kubernetes in regulated environments (e.g., Fraud Prevention or AML).
- Experience designing and deploying end-to-end ML workflows on GCP for fraud detection, transaction monitoring, or risk scoring.
- Experience running high-throughput, low-latency ML prediction services at scale.
- Experience building real-time ML systems for fast decisioning, alerting, or scoring in fraud and financial crime prevention.
- Strong statistical skills applied to fraud analytics, anomaly detection, and risk modeling, with good software design for scalable systems.
- Experience using Infrastructure as Code (Terraform) to manage and secure GCP infrastructure in compliance-driven environments.
Benefits
- Extensive training and learning opportunities.
- Work-life balance.
- International opportunities and working environment.
- Access to SEB staff banking with exclusive benefits.
- Innovative company in forefront of technology.
Tags & focus areas
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