Senior Machine Learning Engineer

Role overview A senior machine learning engineering role on a freelance contract basis, fully remote from Brazil. The position owns models from handoff through to production on a shared central ML platform, with direct responsibility for serving, monitoring, and the standards that scale the platform across the organization.

Responsibilities - Own models from handoff to production, including packaging, deployment, monitoring, and the go-live decision - Maintain production reliability through drift detection, performance monitoring, alerting, and incident response - Manage the serving and inference path, including pipeline artifacts, inference entry points, monitoring hooks, and feature-store parity - Review model design and evaluation methodology to catch data leakage, backward-window errors, and weak evaluation early - Extend the shared ML platform without introducing project-specific logic into shared code - Set the engineering standards the platform runs on as it scales

Requirements - 2+ years in production machine learning engineering with real post-handoff ownership of models - Strong Python skills producing typed, tested, production-grade code, including peer review - Sufficient ML depth to challenge problem framing, feature engineering, model selection, and evaluation methodology - Hands-on experience with a managed ML platform such as SageMaker, Vertex AI, Databricks, or Azure ML - Experience with feature stores, ML CI/CD pipelines, AWS, and Terraform - AI-native daily workflow using tools like Claude, ChatGPT, or Copilot - English at C1 level, written and spoken - Based in Brazil with own company for direct B2B invoicing

Nice to have - Snowflake and dbt experience - Mentorship or peer review experience - Comfort working where the answer is not yet defined

Benefits and work setup - 40 hours per week, Monday through Friday, invoiced monthly against own company - Fully remote from anywhere in Brazil with daily overlap into the Berlin working day - Treated as a full team member with standups and bi-weekly sprints - English-speaking engineering team with short decision paths - Direct ownership of models serving a live production product - Structured onboarding with a team buddy