ML Engineer

Must-have skills

1.      Production data pipelines: built and operated batch and streaming pipelines with Spark/PySpark and Kafka. 

2.      ML training: trained, evaluated and shipped models to production (gradient boosting, anomaly detection, sequence or graph models), with sound evaluation and attention to label quality.

3.      Feature management: designed and run feature stores or feature pipelines, including point-in-time joins, feature versioning, training/serving consistency and backfills.

4.      Model serving and MLOps: deployed models behind batch and real-time inference, with CI/CD, model registry, monitoring and rollback.

5.      Python as primary language (production services, PySpark, pytest), plus strong SQL and data modeling on Iceberg/Hive tables with Athena or Databricks.

6.      Data analysis: profiling messy telemetry, checking signal quality, and validating score distributions against expectations