ML Engineer (Python/Big Data)

What we expect in general?

• Strong Python and production ML skills, with a proven track record of shipping models into real production pipelines.
• Hands-on experience using classic ML to surface data quality issues at scale: unsupervised anomaly detection (kNN, Isolation Forest, autoencoders) and clustering on messy real-world tabular data.
• Practical experience pairing classic ML with LLMs: using models to flag suspicious records and LLMs for reasoning, false-positive filtering, and the final verification of anomalies.
• Solid data engineering background across the modern stack (Airflow, Spark/Dataproc, BigQuery, Snowflake, Iceberg/Trino) and the production toolchain (GCP, Docker, Terraform, CI, MLflow).
• Pragmatic, product-oriented approach focused on incremental value delivery and seamless integration into existing workflows.
• Professional fluency in English, enabling smooth technical and business discussions in an international environment.