ML Engineer

Arkhya Tech Inc.; Mountain View (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Arkhya Tech Inc. in Mountain View, CA is seeking an experienced ML Engineer to build feature pipelines on EMR Serverless and streaming systems, manage feature stores, and train and evaluate models for insider-risk scoring and anomaly detection.

Responsibilities include deploying models on the Intuit ML Platform with batch and real-time inference, ensuring explainability, and coordinating with data engineering to move from rules to ML scoring across FY27. Strong Python and SQL skills are required.

Qualifications

• Experience building and operating batch and streaming data pipelines with Spark/PySpark and Kafka.
• Experience in ML training, evaluation and shipping models to production with proper label quality.
• Experience designing feature stores or pipelines, including point-in-time joins, versioning and training/serving consistency.
• Experience deploying models behind batch and real-time inference with CI/CD, model registry, monitoring and rollback.
• Proficient in Python (production services, PySpark, pytest) and strong SQL; data modeling on Iceberg/Hive with Athena or Databricks.

Responsibilities

• Build feature pipelines on 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, ensuring training and inference read the same features.
• Train and evaluate models for insider-risk scoring and anomaly detection: data prep, labeling with analyst team, experiment tracking, evaluation on precision, recall and alert volume.
• Deploy and serve models on the Intuit ML Platform: batch inference for the daily composite score, real-time inference through MXS and MSaaS, and LLM-based enrichment through LXS.
• 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, monitoring, runbooks and on-call.
• Work with the data engineering and analytics teams to move the application from weighted rules to ML scoring across FY27, including non-human identity (NHI) scoring.

Skills

Production data pipelines

ML training

Feature management

Model serving

Python

SQL

Tools

Spark/PySpark

Kafka

Iceberg/Hive

Athena/Databricks

Job description

• Build feature pipelines on 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 models for 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 models on the Intuit ML Platform: batch inference for the daily composite score, real-time inference through MXS (Model Execution Service) and MSaaS, and LLM-based enrichment through LXS (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 teams to move the application from weighted rules to ML scoring across FY27, including non-human identity (NHI) scoring.

Must-have skills

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

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