Full-Stack Machine Learning Engineer

A full‑stack machine learning engineer develops and deploys ML‑powered services, tools, and applications supporting fraud detection and identity analytics. They work across backend systems, model‑serving pipelines, and user interfaces, ensuring integration and real‑time performance. Responsibilities include creating inference APIs, building data pipelines, and supporting model evaluation processes. Candidates should have at least four years of software engineering experience, strong Python and Java skills, and familiarity with ML model deployment and data platforms like Snowflake. They are expected to possess a sense of ownership, independence, and knowledge of DevOps and secure engineering practices. The role entails collaborating with data scientists, architects, and QA teams to optimise ML solutions within a secure, efficient technical environment.