Staff Machine Learning Engineer

About the role

This role is ideal for a pragmatic and impact‑driven builder who is passionate about bridging the gap between data science and production systems. You will collaborate closely with Data Scientists to bring predictive models to life, establishing robust deployment pipelines, setting up foundational infrastructure, and driving MLOps best practices.

Key responsibilities

• Translate experimental code from Data Scientists and Data Analysts into production‑ready architectures, handling code refactoring and model hosting decisions.
• Design and formalize end‑to‑end model deployment processes, including versioning management for reproducibility.
• Build and maintain robust data pipelines using modern big‑data and distributed processing ecosystems.
• Implement foundational ML infrastructure such as feature stores and data versioning tools to support scalable growth.
• Monitor, maintain, and optimize deployed solutions, addressing technical debt and ensuring high availability.

Required profile

• Bachelor's or Master's degree in a quantitative discipline with 5+ years of software/ML engineering experience.
• Hands‑on experience in MLOps, containerization (Docker, Kubernetes) and model hosting (FastAPI, REST APIs).
• Proficiency in Python, SQL and distributed data processing frameworks (e.g., Spark, Hive).
• Experience setting up feature stores, data versioning tools (e.g., DVC) and model registry systems.
• Strong software engineering practices including Git, CI/CD pipelines and clean architecture design.

Required skills

• Python
• SQL
• Apache Spark / Hive
• Docker
• Kubernetes
• FastAPI or REST API development
• DVC (data version control)
• Git and CI/CD
• Feature store integration
• Model registry/versioning