Data/ML Engineer

AI-native platform

Data/ML Engineer

• Design and build

  data pipelines

  to transform raw events (visits, purchases, campaigns) into usable features.
• Define and compute

  labels

  (e.g., churn, upsell, lead quality) from historical events.
• Develop and train

  ML models

  (e.g., churn prediction, upsell propensity, lead scoring) using Python (scikit-learn, LightGBM, XGBoost).
• Build

  real-time inference services

  to serve predictions into production systems.
• Set up retraining and monitoring pipelines (Airflow, MLflow, or similar).
• Collaborate with backend engineers to integrate model outputs into Gleantap workflows.
• Ensure data quality, reproducibility, and compliance (HIPAA for healthcare customers).

• 3–5+ years of experience in data engineering or applied ML.
• Strong proficiency in

  Python

  , SQL, and one or more ML libraries (scikit-learn, LightGBM, XGBoost, PyTorch).
• Experience with

  data pipelines

  (Airflow, dbt, or custom ETL).
• Comfortable with

  event-driven systems

  (Kafka, Redis, ClickHouse or similar OLAP).
• Understanding of ML lifecycle: training, serving, monitoring, retraining.
• Ability to design

  time-based labels

  (avoiding data leakage).
• Strong problem-solving skills and eagerness to work in a startup environment.

• MLOps tools (MLflow, BentoML, Ray Serve).
• Experience with

  bandit algorithms, A/B testing, or uplift modeling

  .
• Prior work with customer engagement, CRM, or subscription businesses.