ML Engineer

Build feature pipelineson EMR Serverless Spark and Event streaming that turn lake data into versioned, point-in-time-correct behavioral features per user and per identity. Own feature management:feature definitions, registry, backfills, freshness and drift monitoring, and making sure training and inference read the same features. Train and evaluate modelsfor insider-risk scoring and anomaly detection: data prep, labeling with the analyst team, experiment tracking, and evaluation on precision, recall and alert volume. Deploy and serve modelson the Intuit ML Platform: batch inference for the daily composite score, real-time inference throughMXS(Model Execution Service) andMSaaS, and LLM-based enrichment throughLXS(LLM Execution Service). Keep scores explainable:per-feature contributions and evidence links that an analyst can follow back to source events. Run the ML lifecycle:retraining schedules, model versioning, shadow and A/B rollout against the rule-based score, monitoring, runbooks and on-call. Work with the data engineering and analytics teamsto move the application from weighted rules to ML scoring across FY27, including non-human identity (NHI) scoring. Must-have skillsProduction data pipelines:built and operated batch and streaming pipelines with Spark/PySpark and Kafka. 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. Feature management:designed and run feature stores or feature pipelines, including point-in-time joins, feature versioning, training/serving consistency and backfills. Model serving and MLOps:deployed models behind batch and real-time inference, with CI/CD, model registry, monitoring and rollback. Pythonas primary language (production services, PySpark, pytest), plus strongSQLand data modeling on Iceberg/Hive tables with Athena or Databricks. Data analysis:profiling messy telemetry, checking signal quality, and validating score distributions against expectations