Responsibilities

📍Design and implement large-scale, distributed ML training pipelines. 📍Build scalable infrastructure for data preprocessing, feature engineering, and model evaluation. 📍Lead the technical design and development of new ML systems: from architecture to production. 📍Collaborate cross-functionally with DS, infra teams, Product, BA and Engineering teams to define and deliver impactful solutions. 📍Own the full lifecycle of ML infra: tooling, versioning, monitoring, automation, measuring results and quickly responding to critical issues. 📍Continuously research and adopt best-in-class practices in MLOps, performance tuning, and distributed systems.

Requirements

📍B.Sc. or M.Sc. in Computer Science, Software Engineering, or other equivalents fields. 📍5+ years of hands-on experience in backend or ML engineering. 📍Strong Python skills and experience working with distributed systems and parallel data processing frameworks such as Spark (using PySpark or Scala), Dask, or similar technologies. Familiarity with Scala is a strong advantage, especially in performance critical. 📍Proven track record in designing and scaling ML infrastructure. 📍Deep understanding of ML workflows and lifecycle management. 📍Experience in cloud environments (AWS, GCP, OCI) and containerized deployment (Kubernetes).

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