Role overview
- 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.
What you'll work on
- 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.
Tags & focus areas
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